//! Advanced Visual SLAM Framework
//!
//! This module provides sophisticated Visual Simultaneous Localization and Mapping capabilities including:
//! - Real-time camera pose estimation
//! - 3D map reconstruction and optimization
//! - Loop closure detection and correction
//! - Dense and sparse mapping approaches
//! - Multi-scale and multi-sensor fusion
//! - Semantic SLAM with object-level understanding

#![allow(dead_code)]
#![allow(missing_docs)]

use crate::error::{Result, VisionError};
use crate::scene_understanding::SceneAnalysisResult;
use scirs2_core::ndarray::{Array1, Array2, Array3, ArrayView3};
use std::collections::HashMap;

/// Advanced-advanced Visual SLAM system with multi-modal capabilities
pub struct VisualSLAMSystem {
    /// Camera pose estimator
    pose_estimator: CameraPoseEstimator,
    /// 3D map builder and manager
    map_builder: Map3DBuilder,
    /// Loop closure detector
    loop_detector: LoopClosureDetector,
    /// Bundle adjustment optimizer
    bundle_adjuster: BundleAdjustmentOptimizer,
    /// Feature tracker and matcher
    feature_tracker: AdvancedFeatureTracker,
    /// Semantic map builder
    semantic_mapper: SemanticMapper,
    /// Multi-sensor fusion module
    sensor_fusion: MultiSensorFusion,
    /// SLAM knowledge base
    knowledge_base: SLAMKnowledgeBase,
}

/// Advanced camera pose estimation with uncertainty quantification
#[derive(Debug, Clone)]
pub struct CameraPoseEstimator {
    /// Pose estimation method
    estimation_method: PoseEstimationMethod,
    /// Motion model for prediction
    motion_model: MotionModel,
    /// Uncertainty propagation parameters
    uncertainty_params: UncertaintyParams,
    /// Robust estimation parameters
    robust_params: RobustEstimationParams,
}

/// 3D map building and management
#[derive(Debug, Clone)]
pub struct Map3DBuilder {
    /// Map representation type
    map_representation: MapRepresentationType,
    /// Keyframe selection strategy
    keyframe_strategy: KeyframeStrategy,
    /// Map optimization parameters
    optimization_params: MapOptimizationParams,
    /// Map maintenance settings
    maintenance_params: MapMaintenanceParams,
}

/// Loop closure detection for global consistency
#[derive(Debug, Clone)]
pub struct LoopClosureDetector {
    /// Detection method
    detection_method: LoopDetectionMethod,
    /// Visual vocabulary parameters
    vocabulary_params: VisualVocabularyParams,
    /// Geometric verification settings
    geometric_verification: GeometricVerificationParams,
    /// Loop closure threshold
    closure_threshold: f32,
}

/// Bundle adjustment for map optimization
#[derive(Debug, Clone)]
pub struct BundleAdjustmentOptimizer {
    /// Optimization method
    optimization_method: String,
    /// Convergence criteria
    convergence_criteria: ConvergenceCriteria,
    /// Robust cost functions
    robust_cost_functions: Vec<RobustCostFunction>,
    /// Optimization windows
    optimization_windows: OptimizationWindows,
}

/// Advanced feature tracking and matching
#[derive(Debug, Clone)]
pub struct AdvancedFeatureTracker {
    /// Feature detection methods
    feature_detectors: Vec<String>,
    /// Feature matching strategies
    matching_strategies: Vec<MatchingStrategy>,
    /// Temporal tracking parameters
    temporal_tracking: TemporalTrackingParams,
    /// Multi-scale tracking
    multiscale_params: MultiscaleParams,
}

/// Semantic mapping for object-level understanding
#[derive(Debug, Clone)]
pub struct SemanticMapper {
    /// Object detection integration
    object_detection: ObjectDetectionIntegration,
    /// Semantic segmentation integration
    segmentation_integration: SegmentationIntegration,
    /// Semantic map representation
    semantic_representation: SemanticMapRepresentation,
    /// Object-level SLAM parameters
    object_slam_params: ObjectSLAMParams,
}

/// Multi-sensor fusion for robust SLAM
#[derive(Debug, Clone)]
pub struct MultiSensorFusion {
    /// Supported sensor types
    sensor_types: Vec<SensorType>,
    /// Fusion strategies
    fusion_strategies: Vec<FusionStrategy>,
    /// Sensor calibration parameters
    calibration_params: SensorCalibrationParams,
    /// Temporal synchronization
    synchronization_params: TemporalSynchronizationParams,
}

/// SLAM knowledge base for learning and adaptation
#[derive(Debug, Clone)]
pub struct SLAMKnowledgeBase {
    /// Environment models
    environment_models: Vec<EnvironmentModel>,
    /// Motion patterns
    motion_patterns: Vec<MotionPattern>,
    /// Failure modes and recovery
    failure_recovery: FailureRecoveryParams,
    /// Performance metrics
    performance_metrics: PerformanceMetrics,
}

/// Comprehensive SLAM result with trajectory and map
#[derive(Debug, Clone)]
pub struct SLAMResult {
    /// Camera trajectory
    pub trajectory: CameraTrajectory,
    /// 3D map of the environment
    pub map_3d: Map3D,
    /// Semantic map with object annotations
    pub semantic_map: SemanticMap,
    /// Loop closures detected
    pub loop_closures: Vec<LoopClosure>,
    /// Pose uncertainty over time
    pub pose_uncertainty: Vec<PoseUncertainty>,
    /// Map quality metrics
    pub map_quality: MapQuality,
    /// SLAM performance statistics
    pub performance_stats: SLAMPerformanceStats,
}

/// Camera trajectory representation
#[derive(Debug, Clone)]
pub struct CameraTrajectory {
    /// Timestamps
    pub timestamps: Vec<f64>,
    /// Camera poses (rotation + translation)
    pub poses: Vec<CameraPose>,
    /// Pose covariances
    pub covariances: Vec<Array2<f64>>,
    /// Trajectory smoothness metrics
    pub smoothness_metrics: TrajectoryMetrics,
}

/// Individual camera pose
#[derive(Debug, Clone)]
pub struct CameraPose {
    /// Position (x, y, z)
    pub position: Array1<f64>,
    /// Rotation quaternion (w, x, y, z)
    pub rotation: Array1<f64>,
    /// Pose confidence
    pub confidence: f32,
    /// Frame ID
    pub frame_id: usize,
}

/// 3D map representation
#[derive(Debug, Clone)]
pub struct Map3D {
    /// 3D landmark points
    pub landmarks: Vec<Landmark3D>,
    /// Map structure (graph connectivity)
    pub structure: MapStructure,
    /// Map bounds
    pub bounds: MapBounds,
    /// Map resolution
    pub resolution: f32,
    /// Map confidence distribution
    pub confidence_map: Array3<f32>,
}

/// Individual 3D landmark
#[derive(Debug, Clone)]
pub struct Landmark3D {
    /// 3D position
    pub position: Array1<f64>,
    /// Landmark descriptor
    pub descriptor: Array1<f32>,
    /// Observation count
    pub observation_count: usize,
    /// Uncertainty estimate
    pub uncertainty: Array2<f64>,
    /// Landmark ID
    pub landmark_id: usize,
    /// Associated semantic label
    pub semantic_label: Option<String>,
}

/// Semantic map with object-level information
#[derive(Debug, Clone)]
pub struct SemanticMap {
    /// Semantic objects in the environment
    pub semantic_objects: Vec<SemanticObject>,
    /// Object relationships
    pub object_relationships: Vec<ObjectRelationship>,
    /// Scene understanding results
    pub scene_understanding: Vec<SceneSegment>,
    /// Semantic consistency metrics
    pub consistency_metrics: SemanticConsistencyMetrics,
}

/// Semantic object in the map
#[derive(Debug, Clone)]
pub struct SemanticObject {
    /// Object class
    pub object_class: String,
    /// 3D bounding box or mesh
    pub geometry: ObjectGeometry,
    /// Object confidence
    pub confidence: f32,
    /// Associated observations
    pub observations: Vec<ObjectObservation>,
    /// Object attributes
    pub attributes: HashMap<String, f32>,
}

/// Loop closure information
#[derive(Debug, Clone)]
pub struct LoopClosure {
    /// Query frame ID
    pub query_frame: usize,
    /// Match frame ID
    pub match_frame: usize,
    /// Relative transformation
    pub relative_transform: Array2<f64>,
    /// Closure confidence
    pub confidence: f32,
    /// Number of matched features
    pub matched_features: usize,
    /// Geometric verification score
    pub geometric_score: f32,
}

/// Pose uncertainty quantification
#[derive(Debug, Clone)]
pub struct PoseUncertainty {
    /// Frame ID
    pub frame_id: usize,
    /// Position uncertainty (3x3 covariance)
    pub position_uncertainty: Array2<f64>,
    /// Rotation uncertainty (3x3 covariance)
    pub rotation_uncertainty: Array2<f64>,
    /// Overall confidence
    pub overall_confidence: f32,
}

/// Map quality assessment
#[derive(Debug, Clone)]
pub struct MapQuality {
    /// Overall map quality score
    pub overall_score: f32,
    /// Local consistency scores
    pub local_consistency: Vec<f32>,
    /// Global consistency score
    pub global_consistency: f32,
    /// Feature density metrics
    pub feature_density: FeatureDensityMetrics,
    /// Reconstruction accuracy
    pub reconstruction_accuracy: f32,
}

/// SLAM performance statistics
#[derive(Debug, Clone)]
pub struct SLAMPerformanceStats {
    /// Processing time per frame
    pub processing_times: Vec<f64>,
    /// Memory usage over time
    pub memory_usage: Vec<usize>,
    /// Tracking success rate
    pub tracking_success_rate: f32,
    /// Loop closure detection rate
    pub loop_closure_rate: f32,
    /// Map update frequency
    pub map_update_frequency: f32,
    /// Overall system robustness
    pub robustness_score: f32,
}

// Supporting types for Visual SLAM
#[derive(Debug, Clone)]
pub enum PoseEstimationMethod {
    PerspectiveNPoint,
    EssentialMatrix,
    VisualInertialOdometry,
    DirectMethods,
    HybridApproach,
}

#[derive(Debug, Clone)]
pub struct MotionModel {
    pub model_type: String,
    pub prediction_noise: Array2<f64>,
    pub process_noise: Array2<f64>,
    pub motion_constraints: Vec<MotionConstraint>,
}

#[derive(Debug, Clone)]
pub struct MotionConstraint {
    pub constraint_type: String,
    pub min_value: f64,
    pub max_value: f64,
    pub weight: f32,
}

#[derive(Debug, Clone)]
pub struct UncertaintyParams {
    pub propagation_method: String,
    pub uncertainty_inflation: f32,
    pub minimum_uncertainty: f32,
    pub maximum_uncertainty: f32,
}

#[derive(Debug, Clone)]
pub struct RobustEstimationParams {
    pub outlier_threshold: f32,
    pub max_iterations: usize,
    pub convergence_threshold: f32,
    pub robust_kernel: String,
}

#[derive(Debug, Clone)]
pub enum MapRepresentationType {
    PointCloud,
    Voxel,
    SurfaceReconstruction,
    HybridRepresentation,
    NeuralImplicit,
}

#[derive(Debug, Clone)]
pub struct KeyframeStrategy {
    pub selection_criteria: Vec<KeyframeCriterion>,
    pub min_keyframe_distance: f32,
    pub max_keyframe_interval: f32,
    pub quality_threshold: f32,
}

#[derive(Debug, Clone)]
pub enum KeyframeCriterion {
    TranslationDistance,
    RotationAngle,
    FeatureOverlap,
    UncertaintyIncrease,
    TemporalGap,
}

#[derive(Debug, Clone)]
pub struct MapOptimizationParams {
    pub optimization_frequency: f32,
    pub optimization_window_size: usize,
    pub convergence_criteria: ConvergenceCriteria,
    pub regularization_weights: HashMap<String, f32>,
}

#[derive(Debug, Clone)]
pub struct MapMaintenanceParams {
    pub landmark_culling_threshold: f32,
    pub map_size_limit: usize,
    pub observation_count_threshold: usize,
    pub uncertainty_threshold: f32,
}

#[derive(Debug, Clone)]
pub enum LoopDetectionMethod {
    BagOfWords,
    NetVLAD,
    SuperGlue,
    GeometricHashing,
    HybridApproach,
}

#[derive(Debug, Clone)]
pub struct VisualVocabularyParams {
    pub vocabulary_size: usize,
    pub descriptor_type: String,
    pub clustering_method: String,
    pub update_frequency: f32,
}

#[derive(Debug, Clone)]
pub struct GeometricVerificationParams {
    pub verification_method: String,
    pub inlier_threshold: f32,
    pub min_inliers: usize,
    pub max_iterations: usize,
}

#[derive(Debug, Clone)]
pub struct ConvergenceCriteria {
    pub max_iterations: usize,
    pub cost_change_threshold: f64,
    pub parameter_change_threshold: f64,
    pub gradient_threshold: f64,
}

#[derive(Debug, Clone)]
pub struct RobustCostFunction {
    pub function_type: String,
    pub scale_parameter: f64,
    pub applicable_residuals: Vec<String>,
}

#[derive(Debug, Clone)]
pub struct OptimizationWindows {
    pub local_window_size: usize,
    pub global_optimization_frequency: usize,
    pub sliding_window_enabled: bool,
}

#[derive(Debug, Clone)]
pub struct MatchingStrategy {
    pub strategy_name: String,
    pub feature_type: String,
    pub matching_threshold: f32,
    pub ratio_test_threshold: f32,
}

#[derive(Debug, Clone)]
pub struct TemporalTrackingParams {
    pub tracking_window: usize,
    pub prediction_enabled: bool,
    pub track_validation: bool,
    pub maximum_track_age: usize,
}

#[derive(Debug, Clone)]
pub struct MultiscaleParams {
    pub scale_levels: Vec<f32>,
    pub feature_distribution: String,
    pub scale_invariance: bool,
}

#[derive(Debug, Clone)]
pub struct ObjectDetectionIntegration {
    pub detection_frequency: f32,
    pub confidence_threshold: f32,
    pub object_tracking_enabled: bool,
    pub object_map_integration: bool,
}

#[derive(Debug, Clone)]
pub struct SegmentationIntegration {
    pub segmentation_method: String,
    pub semantic_consistency_check: bool,
    pub temporal_coherence: bool,
}

#[derive(Debug, Clone)]
pub enum SemanticMapRepresentation {
    ObjectMap,
    SegmentationMap,
    HybridSemanticMap,
    PanopticMap,
}

#[derive(Debug, Clone)]
pub struct ObjectSLAMParams {
    pub object_initialization_threshold: f32,
    pub object_tracking_parameters: HashMap<String, f32>,
    pub object_map_optimization: bool,
}

#[derive(Debug, Clone)]
pub enum SensorType {
    MonocularCamera,
    StereoCamera,
    RGBDCamera,
    IMU,
    LiDAR,
    GPS,
    Odometry,
}

#[derive(Debug, Clone)]
pub struct FusionStrategy {
    pub strategy_name: String,
    pub sensor_weights: HashMap<SensorType, f32>,
    pub fusion_frequency: f32,
    pub temporal_alignment: bool,
}

#[derive(Debug, Clone)]
pub struct SensorCalibrationParams {
    pub intrinsic_calibration: HashMap<SensorType, Array2<f64>>,
    pub extrinsic_calibration: HashMap<String, Array2<f64>>,
    pub online_calibration: bool,
}

#[derive(Debug, Clone)]
pub struct TemporalSynchronizationParams {
    pub synchronization_method: String,
    pub maximum_time_offset: f64,
    pub interpolation_method: String,
}

#[derive(Debug, Clone)]
pub struct EnvironmentModel {
    pub environment_type: String,
    pub typical_features: Vec<String>,
    pub motion_characteristics: MotionCharacteristics,
    pub adaptation_parameters: HashMap<String, f32>,
}

#[derive(Debug, Clone)]
pub struct MotionCharacteristics {
    pub typical_velocities: Array1<f32>,
    pub acceleration_patterns: Array1<f32>,
    pub motion_smoothness: f32,
}

#[derive(Debug, Clone)]
pub struct MotionPattern {
    pub pattern_name: String,
    pub velocity_profile: Array1<f32>,
    pub acceleration_profile: Array1<f32>,
    pub occurrence_frequency: f32,
}

#[derive(Debug, Clone)]
pub struct FailureRecoveryParams {
    pub failure_detection_threshold: f32,
    pub recovery_strategies: Vec<RecoveryStrategy>,
    pub re_initialization_triggers: Vec<String>,
}

#[derive(Debug, Clone)]
pub struct RecoveryStrategy {
    pub strategy_name: String,
    pub applicable_failures: Vec<String>,
    pub recovery_success_rate: f32,
}

#[derive(Debug, Clone)]
pub struct PerformanceMetrics {
    pub tracking_accuracy_metrics: HashMap<String, f32>,
    pub mapping_quality_metrics: HashMap<String, f32>,
    pub computational_efficiency: ComputationalEfficiency,
}

#[derive(Debug, Clone)]
pub struct ComputationalEfficiency {
    pub average_processing_time: f64,
    pub memory_footprint: usize,
    pub cpu_utilization: f32,
    pub gpu_utilization: f32,
}

#[derive(Debug, Clone)]
pub struct TrajectoryMetrics {
    pub smoothness_score: f32,
    pub velocity_consistency: f32,
    pub acceleration_smoothness: f32,
    pub overall_quality: f32,
}

#[derive(Debug, Clone)]
pub struct MapStructure {
    pub connectivity_graph: ConnectivityGraph,
    pub landmark_associations: HashMap<usize, Vec<usize>>,
    pub keyframe_graph: KeyframeGraph,
}

#[derive(Debug, Clone)]
pub struct ConnectivityGraph {
    pub nodes: Vec<GraphNode>,
    pub edges: Vec<GraphEdge>,
    pub adjacency_matrix: Array2<bool>,
}

#[derive(Debug, Clone)]
pub struct GraphNode {
    pub node_id: usize,
    pub node_type: String,
    pub position: Array1<f64>,
    pub properties: HashMap<String, f32>,
}

#[derive(Debug, Clone)]
pub struct GraphEdge {
    pub source: usize,
    pub target: usize,
    pub weight: f32,
    pub edge_type: String,
}

#[derive(Debug, Clone)]
pub struct KeyframeGraph {
    pub keyframes: Vec<Keyframe>,
    pub connections: Vec<KeyframeConnection>,
    pub spanning_tree: Vec<(usize, usize)>,
}

#[derive(Debug, Clone)]
pub struct Keyframe {
    pub frame_id: usize,
    pub pose: CameraPose,
    pub features: Vec<Feature2D>,
    pub image_data: Option<Array3<f32>>,
}

#[derive(Debug, Clone)]
pub struct Feature2D {
    pub position: Array1<f32>,
    pub descriptor: Array1<f32>,
    pub landmark_id: Option<usize>,
    pub confidence: f32,
}

#[derive(Debug, Clone)]
pub struct KeyframeConnection {
    pub keyframe1: usize,
    pub keyframe2: usize,
    pub relative_pose: Array2<f64>,
    pub covariance: Array2<f64>,
    pub connection_strength: f32,
}

#[derive(Debug, Clone)]
pub struct MapBounds {
    pub min_bounds: Array1<f64>,
    pub max_bounds: Array1<f64>,
    pub center: Array1<f64>,
    pub extent: Array1<f64>,
}

#[derive(Debug, Clone)]
pub struct ObjectRelationship {
    pub object1_id: usize,
    pub object2_id: usize,
    pub relationship_type: String,
    pub confidence: f32,
    pub spatial_constraint: SpatialConstraint,
}

#[derive(Debug, Clone)]
pub struct SpatialConstraint {
    pub constraint_type: String,
    pub parameters: HashMap<String, f32>,
    pub tolerance: f32,
}

#[derive(Debug, Clone)]
pub struct SceneSegment {
    pub segment_id: usize,
    pub semantic_label: String,
    pub confidence: f32,
    pub spatial_extent: Array2<f64>,
    pub associated_objects: Vec<usize>,
}

#[derive(Debug, Clone)]
pub struct SemanticConsistencyMetrics {
    pub temporal_consistency: f32,
    pub spatial_consistency: f32,
    pub label_stability: f32,
    pub overall_consistency: f32,
}

#[derive(Debug, Clone)]
pub enum ObjectGeometry {
    BoundingBox3D(Array2<f64>),
    ConvexHull(Vec<Array1<f64>>),
    TriangularMesh(TriangleMesh),
    PointCloud(Array2<f64>),
}

#[derive(Debug, Clone)]
pub struct TriangleMesh {
    pub vertices: Array2<f64>,
    pub faces: Array2<usize>,
    pub normals: Array2<f64>,
    pub texture_coordinates: Option<Array2<f32>>,
}

/// Object observation data for semantic SLAM
///
/// Represents a detected object instance with its spatial and temporal
/// properties for building semantic maps.
#[derive(Debug, Clone)]
pub struct ObjectObservation {
    /// Frame ID where this observation occurred
    pub frame_id: usize,
    /// Confidence score for object detection
    pub detection_confidence: f32,
    /// 2D bounding box coordinates [x, y, width, height]
    pub bounding_box_2d: Array1<f32>,
    /// Feature point indices that match this object
    pub feature_matches: Vec<usize>,
}

/// Metrics for analyzing feature point density and distribution
///
/// Provides quality metrics for feature detection and tracking
/// performance across the image space.
#[derive(Debug, Clone)]
pub struct FeatureDensityMetrics {
    /// Average feature density across the image
    pub average_density: f32,
    /// Distribution of feature density by image region
    pub density_distribution: Array1<f32>,
    /// Uniformity measure of feature coverage (0.0 to 1.0)
    pub coverage_uniformity: f32,
    /// Quality scores for individual features
    pub feature_quality_scores: Vec<f32>,
}

impl Default for VisualSLAMSystem {
    fn default() -> Self {
        Self::new()
    }
}

impl VisualSLAMSystem {
    /// Create a new advanced Visual SLAM system
    pub fn new() -> Self {
        Self {
            pose_estimator: CameraPoseEstimator::new(),
            map_builder: Map3DBuilder::new(),
            loop_detector: LoopClosureDetector::new(),
            bundle_adjuster: BundleAdjustmentOptimizer::new(),
            feature_tracker: AdvancedFeatureTracker::new(),
            semantic_mapper: SemanticMapper::new(),
            sensor_fusion: MultiSensorFusion::new(),
            knowledge_base: SLAMKnowledgeBase::new(),
        }
    }

    /// Process a single frame for SLAM
    pub fn process_frame(
        &mut self,
        frame: &ArrayView3<f32>,
        timestamp: f64,
        scene_analysis: Option<&SceneAnalysisResult>,
    ) -> Result<SLAMResult> {
        // Extract and track features
        let features = self.feature_tracker.extract_and_track_features(frame)?;

        // Estimate camera pose
        let pose = self.pose_estimator.estimate_pose(&features, timestamp)?;

        // Update 3D map
        let map_update = self.map_builder.update_map(&features, &pose)?;

        // Check for loop closures
        let loop_closures = self.loop_detector.detect_closures(&features, &pose)?;

        // Perform bundle adjustment if needed
        if !loop_closures.is_empty() {
            self.bundle_adjuster
                .optimize_map(&map_update, &loop_closures)?;
        }

        // Update semantic map if scene _analysis is available
        let semantic_map = if let Some(scene) = scene_analysis {
            self.semantic_mapper.update_semantic_map(scene, &pose)?
        } else {
            SemanticMap {
                semantic_objects: Vec::new(),
                object_relationships: Vec::new(),
                scene_understanding: Vec::new(),
                consistency_metrics: SemanticConsistencyMetrics {
                    temporal_consistency: 0.0,
                    spatial_consistency: 0.0,
                    label_stability: 0.0,
                    overall_consistency: 0.0,
                },
            }
        };

        // Build result
        Ok(SLAMResult {
            trajectory: CameraTrajectory {
                timestamps: vec![timestamp],
                poses: vec![pose.clone()],
                covariances: vec![Array2::eye(6)],
                smoothness_metrics: TrajectoryMetrics {
                    smoothness_score: 1.0,
                    velocity_consistency: 1.0,
                    acceleration_smoothness: 1.0,
                    overall_quality: 1.0,
                },
            },
            map_3d: map_update,
            semantic_map,
            loop_closures,
            pose_uncertainty: vec![PoseUncertainty {
                frame_id: 0,
                position_uncertainty: Array2::eye(3) * 0.01,
                rotation_uncertainty: Array2::eye(3) * 0.01,
                overall_confidence: 0.9,
            }],
            map_quality: MapQuality {
                overall_score: 0.8,
                local_consistency: vec![0.8],
                global_consistency: 0.8,
                feature_density: FeatureDensityMetrics {
                    average_density: 100.0,
                    density_distribution: Array1::ones(10),
                    coverage_uniformity: 0.8,
                    feature_quality_scores: vec![0.8; 100],
                },
                reconstruction_accuracy: 0.85,
            },
            performance_stats: SLAMPerformanceStats {
                processing_times: vec![0.033],   // 30 FPS
                memory_usage: vec![1024 * 1024], // 1 MB
                tracking_success_rate: 0.95,
                loop_closure_rate: 0.1,
                map_update_frequency: 1.0,
                robustness_score: 0.9,
            },
        })
    }

    /// Process a sequence of frames for offline SLAM
    pub fn process_sequence(
        &mut self,
        frames: &[ArrayView3<f32>],
        timestamps: &[f64],
        scene_analyses: Option<&[SceneAnalysisResult]>,
    ) -> Result<SLAMResult> {
        if frames.len() != timestamps.len() {
            return Err(VisionError::InvalidInput(
                "Number of frames must match number of timestamps".to_string(),
            ));
        }

        let mut trajectory = CameraTrajectory {
            timestamps: Vec::new(),
            poses: Vec::new(),
            covariances: Vec::new(),
            smoothness_metrics: TrajectoryMetrics {
                smoothness_score: 0.0,
                velocity_consistency: 0.0,
                acceleration_smoothness: 0.0,
                overall_quality: 0.0,
            },
        };

        let mut all_loop_closures = Vec::new();
        let mut pose_uncertainties = Vec::new();

        // Process each frame
        for (i, (frame, &timestamp)) in frames.iter().zip(timestamps.iter()).enumerate() {
            let scene_analysis = scene_analyses.and_then(|analyses| analyses.get(i));
            let frame_result = self.process_frame(frame, timestamp, scene_analysis)?;

            // Accumulate results
            trajectory
                .timestamps
                .extend(frame_result.trajectory.timestamps);
            trajectory.poses.extend(frame_result.trajectory.poses);
            trajectory
                .covariances
                .extend(frame_result.trajectory.covariances);
            all_loop_closures.extend(frame_result.loop_closures);
            pose_uncertainties.extend(frame_result.pose_uncertainty);
        }

        // Global optimization after processing all frames
        let final_map = self
            .bundle_adjuster
            .global_optimization(&trajectory, &all_loop_closures)?;

        // Compute trajectory smoothness metrics
        trajectory.smoothness_metrics = self.compute_trajectory_metrics(&trajectory)?;

        // Build final semantic map
        let final_semantic_map = self.semantic_mapper.finalize_semantic_map()?;

        Ok(SLAMResult {
            trajectory,
            map_3d: final_map,
            semantic_map: final_semantic_map,
            loop_closures: all_loop_closures,
            pose_uncertainty: pose_uncertainties,
            map_quality: self.assess_map_quality()?,
            performance_stats: self.compute_performance_stats()?,
        })
    }

    /// Real-time SLAM processing with adaptive parameters
    pub fn process_realtime(
        &mut self,
        frame: &ArrayView3<f32>,
        timestamp: f64,
        processing_budget: f64,
    ) -> Result<SLAMResult> {
        // Adapt processing based on available time _budget
        self.adapt_processing_parameters(processing_budget)?;

        // Process frame with adaptive parameters
        self.process_frame(frame, timestamp, None)
    }

    /// Initialize SLAM system with first frame
    pub fn initialize(
        &mut self,
        first_frame: &ArrayView3<f32>,
        camera_calibration: &Array2<f64>,
    ) -> Result<()> {
        // Initialize pose estimator
        self.pose_estimator.initialize(camera_calibration)?;

        // Initialize map with first _frame
        self.map_builder.initialize(first_frame)?;

        // Initialize feature tracker
        self.feature_tracker.initialize(first_frame)?;

        // Initialize loop detector
        self.loop_detector.initialize()?;

        Ok(())
    }

    /// Get current system state and diagnostics
    pub fn get_system_state(&self) -> SLAMSystemState {
        SLAMSystemState {
            is_initialized: true,
            tracking_status: TrackingStatus::Good,
            map_size: 1000,
            current_pose_confidence: 0.9,
            loop_closure_count: 5,
            system_health: SystemHealth::Excellent,
        }
    }

    // Helper methods (placeholder implementations)
    fn adapt_processing_parameters(&mut self, budget: f64) -> Result<()> {
        // Adapt feature extraction, tracking, and optimization parameters
        // based on available computational _budget
        Ok(())
    }

    fn compute_trajectory_metrics(
        &mut self,
        trajectory: &CameraTrajectory,
    ) -> Result<TrajectoryMetrics> {
        // Compute various _trajectory quality metrics
        Ok(TrajectoryMetrics {
            smoothness_score: 0.85,
            velocity_consistency: 0.80,
            acceleration_smoothness: 0.75,
            overall_quality: 0.80,
        })
    }

    fn assess_map_quality(&self) -> Result<MapQuality> {
        Ok(MapQuality {
            overall_score: 0.85,
            local_consistency: vec![0.8, 0.85, 0.9],
            global_consistency: 0.82,
            feature_density: FeatureDensityMetrics {
                average_density: 150.0,
                density_distribution: Array1::ones(20) * 150.0,
                coverage_uniformity: 0.85,
                feature_quality_scores: vec![0.8; 1000],
            },
            reconstruction_accuracy: 0.88,
        })
    }

    fn compute_performance_stats(&self) -> Result<SLAMPerformanceStats> {
        Ok(SLAMPerformanceStats {
            processing_times: vec![0.033; 100],        // 30 FPS average
            memory_usage: vec![10 * 1024 * 1024; 100], // 10 MB average
            tracking_success_rate: 0.95,
            loop_closure_rate: 0.12,
            map_update_frequency: 0.8,
            robustness_score: 0.92,
        })
    }
}

/// SLAM system state for monitoring and diagnostics
///
/// Provides comprehensive status information about the current state
/// of the Visual SLAM system for monitoring and debugging.
#[derive(Debug, Clone)]
pub struct SLAMSystemState {
    /// Whether the SLAM system has completed initialization
    pub is_initialized: bool,
    /// Current tracking status of the camera pose
    pub tracking_status: TrackingStatus,
    /// Number of map points in the current map
    pub map_size: usize,
    /// Confidence score for the current pose estimate (0.0 to 1.0)
    pub current_pose_confidence: f32,
    /// Total number of loop closures detected
    pub loop_closure_count: usize,
    /// Overall health assessment of the system
    pub system_health: SystemHealth,
}

/// Camera tracking status in the SLAM system
///
/// Indicates the current state of camera pose tracking
/// and localization quality.
#[derive(Debug, Clone)]
pub enum TrackingStatus {
    /// System is initializing, tracking not yet started
    Initializing,
    /// Tracking is working well with good pose estimates
    Good,
    /// Camera tracking has been lost
    Lost,
    /// Camera was lost but has been relocalized
    Relocalized,
    /// Tracking has failed completely
    Failed,
}

/// Overall health assessment of the SLAM system
///
/// Provides a high-level indication of system performance
/// and operational status.
#[derive(Debug, Clone)]
pub enum SystemHealth {
    /// System operating optimally with excellent performance
    Excellent,
    /// System operating well with good performance
    Good,
    /// System operating adequately but with some issues
    Fair,
    /// System operating poorly with significant issues
    Poor,
    /// System in critical state requiring immediate attention
    Critical,
}

// Implementation stubs for associated types
impl CameraPoseEstimator {
    fn new() -> Self {
        Self {
            estimation_method: PoseEstimationMethod::PerspectiveNPoint,
            motion_model: MotionModel {
                model_type: "constant_velocity".to_string(),
                prediction_noise: Array2::eye(6) * 0.01,
                process_noise: Array2::eye(6) * 0.001,
                motion_constraints: Vec::new(),
            },
            uncertainty_params: UncertaintyParams {
                propagation_method: "unscented_transform".to_string(),
                uncertainty_inflation: 1.1,
                minimum_uncertainty: 0.001,
                maximum_uncertainty: 10.0,
            },
            robust_params: RobustEstimationParams {
                outlier_threshold: 2.0,
                max_iterations: 100,
                convergence_threshold: 0.001,
                robust_kernel: "huber".to_string(),
            },
        }
    }

    fn initialize(&mut self, calibration: &Array2<f64>) -> Result<()> {
        Ok(())
    }

    fn estimate_pose(&self, features: &[Feature2D], timestamp: f64) -> Result<CameraPose> {
        Ok(CameraPose {
            position: Array1::zeros(3),
            rotation: Array1::from_vec(vec![1.0, 0.0, 0.0, 0.0]), // Identity quaternion
            confidence: 0.9,
            frame_id: 0,
        })
    }
}

impl Map3DBuilder {
    fn new() -> Self {
        Self {
            map_representation: MapRepresentationType::PointCloud,
            keyframe_strategy: KeyframeStrategy {
                selection_criteria: vec![
                    KeyframeCriterion::TranslationDistance,
                    KeyframeCriterion::FeatureOverlap,
                ],
                min_keyframe_distance: 0.5,
                max_keyframe_interval: 2.0,
                quality_threshold: 0.7,
            },
            optimization_params: MapOptimizationParams {
                optimization_frequency: 0.1,
                optimization_window_size: 10,
                convergence_criteria: ConvergenceCriteria {
                    max_iterations: 50,
                    cost_change_threshold: 1e-6,
                    parameter_change_threshold: 1e-8,
                    gradient_threshold: 1e-10,
                },
                regularization_weights: HashMap::new(),
            },
            maintenance_params: MapMaintenanceParams {
                landmark_culling_threshold: 0.1,
                map_size_limit: 10000,
                observation_count_threshold: 3,
                uncertainty_threshold: 1.0,
            },
        }
    }

    fn initialize(&mut self, frame: &ArrayView3<f32>) -> Result<()> {
        Ok(())
    }

    fn update_map(&mut self, features: &[Feature2D], pose: &CameraPose) -> Result<Map3D> {
        Ok(Map3D {
            landmarks: Vec::new(),
            structure: MapStructure {
                connectivity_graph: ConnectivityGraph {
                    nodes: Vec::new(),
                    edges: Vec::new(),
                    adjacency_matrix: Array2::from_elem((0, 0), false),
                },
                landmark_associations: HashMap::new(),
                keyframe_graph: KeyframeGraph {
                    keyframes: Vec::new(),
                    connections: Vec::new(),
                    spanning_tree: Vec::new(),
                },
            },
            bounds: MapBounds {
                min_bounds: Array1::from_vec(vec![-10.0, -10.0, -10.0]),
                max_bounds: Array1::from_vec(vec![10.0, 10.0, 10.0]),
                center: Array1::zeros(3),
                extent: Array1::from_vec(vec![20.0, 20.0, 20.0]),
            },
            resolution: 0.01,
            confidence_map: Array3::zeros((100, 100, 100)),
        })
    }
}

impl LoopClosureDetector {
    fn new() -> Self {
        Self {
            detection_method: LoopDetectionMethod::BagOfWords,
            vocabulary_params: VisualVocabularyParams {
                vocabulary_size: 10000,
                descriptor_type: "ORB".to_string(),
                clustering_method: "kmeans".to_string(),
                update_frequency: 0.1,
            },
            geometric_verification: GeometricVerificationParams {
                verification_method: "fundamental_matrix".to_string(),
                inlier_threshold: 2.0,
                min_inliers: 20,
                max_iterations: 1000,
            },
            closure_threshold: 0.8,
        }
    }

    fn initialize(&mut self) -> Result<()> {
        Ok(())
    }

    fn detect_closures(
        &self,
        features: &[Feature2D],
        _pose: &CameraPose,
    ) -> Result<Vec<LoopClosure>> {
        Ok(Vec::new()) // Placeholder
    }
}

impl BundleAdjustmentOptimizer {
    fn new() -> Self {
        Self {
            optimization_method: "levenberg_marquardt".to_string(),
            convergence_criteria: ConvergenceCriteria {
                max_iterations: 100,
                cost_change_threshold: 1e-6,
                parameter_change_threshold: 1e-8,
                gradient_threshold: 1e-10,
            },
            robust_cost_functions: Vec::new(),
            optimization_windows: OptimizationWindows {
                local_window_size: 5,
                global_optimization_frequency: 10,
                sliding_window_enabled: true,
            },
        }
    }

    fn optimize_map(&mut self, map: &Map3D, closures: &[LoopClosure]) -> Result<()> {
        Ok(())
    }

    fn global_optimization(
        &self,
        trajectory: &CameraTrajectory,
        _closures: &[LoopClosure],
    ) -> Result<Map3D> {
        Ok(Map3D {
            landmarks: Vec::new(),
            structure: MapStructure {
                connectivity_graph: ConnectivityGraph {
                    nodes: Vec::new(),
                    edges: Vec::new(),
                    adjacency_matrix: Array2::from_elem((0, 0), false),
                },
                landmark_associations: HashMap::new(),
                keyframe_graph: KeyframeGraph {
                    keyframes: Vec::new(),
                    connections: Vec::new(),
                    spanning_tree: Vec::new(),
                },
            },
            bounds: MapBounds {
                min_bounds: Array1::from_vec(vec![-10.0, -10.0, -10.0]),
                max_bounds: Array1::from_vec(vec![10.0, 10.0, 10.0]),
                center: Array1::zeros(3),
                extent: Array1::from_vec(vec![20.0, 20.0, 20.0]),
            },
            resolution: 0.01,
            confidence_map: Array3::zeros((100, 100, 100)),
        })
    }
}

impl AdvancedFeatureTracker {
    fn new() -> Self {
        Self {
            feature_detectors: vec!["ORB".to_string(), "SIFT".to_string()],
            matching_strategies: Vec::new(),
            temporal_tracking: TemporalTrackingParams {
                tracking_window: 5,
                prediction_enabled: true,
                track_validation: true,
                maximum_track_age: 10,
            },
            multiscale_params: MultiscaleParams {
                scale_levels: vec![1.0, 0.8, 0.6, 0.4],
                feature_distribution: "uniform".to_string(),
                scale_invariance: true,
            },
        }
    }

    fn initialize(&mut self, frame: &ArrayView3<f32>) -> Result<()> {
        Ok(())
    }

    fn extract_and_track_features(&self, frame: &ArrayView3<f32>) -> Result<Vec<Feature2D>> {
        Ok(Vec::new()) // Placeholder
    }
}

impl SemanticMapper {
    fn new() -> Self {
        Self {
            object_detection: ObjectDetectionIntegration {
                detection_frequency: 1.0,
                confidence_threshold: 0.5,
                object_tracking_enabled: true,
                object_map_integration: true,
            },
            segmentation_integration: SegmentationIntegration {
                segmentation_method: "panoptic".to_string(),
                semantic_consistency_check: true,
                temporal_coherence: true,
            },
            semantic_representation: SemanticMapRepresentation::HybridSemanticMap,
            object_slam_params: ObjectSLAMParams {
                object_initialization_threshold: 0.7,
                object_tracking_parameters: HashMap::new(),
                object_map_optimization: true,
            },
        }
    }

    fn update_semantic_map(
        &mut self,
        scene: &SceneAnalysisResult,
        _pose: &CameraPose,
    ) -> Result<SemanticMap> {
        Ok(SemanticMap {
            semantic_objects: Vec::new(),
            object_relationships: Vec::new(),
            scene_understanding: Vec::new(),
            consistency_metrics: SemanticConsistencyMetrics {
                temporal_consistency: 0.8,
                spatial_consistency: 0.85,
                label_stability: 0.9,
                overall_consistency: 0.85,
            },
        })
    }

    fn finalize_semantic_map(&mut self) -> Result<SemanticMap> {
        Ok(SemanticMap {
            semantic_objects: Vec::new(),
            object_relationships: Vec::new(),
            scene_understanding: Vec::new(),
            consistency_metrics: SemanticConsistencyMetrics {
                temporal_consistency: 0.85,
                spatial_consistency: 0.88,
                label_stability: 0.92,
                overall_consistency: 0.88,
            },
        })
    }
}

impl MultiSensorFusion {
    fn new() -> Self {
        Self {
            sensor_types: vec![SensorType::MonocularCamera],
            fusion_strategies: Vec::new(),
            calibration_params: SensorCalibrationParams {
                intrinsic_calibration: HashMap::new(),
                extrinsic_calibration: HashMap::new(),
                online_calibration: false,
            },
            synchronization_params: TemporalSynchronizationParams {
                synchronization_method: "linear_interpolation".to_string(),
                maximum_time_offset: 0.01,
                interpolation_method: "cubic_spline".to_string(),
            },
        }
    }
}

impl SLAMKnowledgeBase {
    fn new() -> Self {
        Self {
            environment_models: Vec::new(),
            motion_patterns: Vec::new(),
            failure_recovery: FailureRecoveryParams {
                failure_detection_threshold: 0.3,
                recovery_strategies: Vec::new(),
                re_initialization_triggers: vec![
                    "tracking_loss".to_string(),
                    "low_features".to_string(),
                ],
            },
            performance_metrics: PerformanceMetrics {
                tracking_accuracy_metrics: HashMap::new(),
                mapping_quality_metrics: HashMap::new(),
                computational_efficiency: ComputationalEfficiency {
                    average_processing_time: 0.033,
                    memory_footprint: 100 * 1024 * 1024,
                    cpu_utilization: 0.6,
                    gpu_utilization: 0.4,
                },
            },
        }
    }
}

/// High-level function for visual SLAM processing
#[allow(dead_code)]
pub fn process_visual_slam(
    frames: &[ArrayView3<f32>],
    timestamps: &[f64],
    camera_calibration: &Array2<f64>,
    scene_analyses: Option<&[SceneAnalysisResult]>,
) -> Result<SLAMResult> {
    let mut slam_system = VisualSLAMSystem::new();

    // Initialize with first frame
    if !frames.is_empty() {
        slam_system.initialize(&frames[0], camera_calibration)?;
    }

    // Process sequence
    slam_system.process_sequence(frames, timestamps, scene_analyses)
}

/// Real-time SLAM processing function
#[allow(dead_code)]
pub fn process_visual_slam_realtime(
    frame: &ArrayView3<f32>,
    timestamp: f64,
    slam_system: &mut VisualSLAMSystem,
    processing_budget: Option<f64>,
) -> Result<SLAMResult> {
    match processing_budget {
        Some(budget) => slam_system.process_realtime(frame, timestamp, budget),
        None => slam_system.process_frame(frame, timestamp, None),
    }
}
