Document Number: SDD-PYTHON-SLAM-001 Version: 1.0 Date: October 2, 2025 Classification: Unclassified Prepared by: Python-SLAM Architecture Team Approved by: [Technical Lead]
| Version | Date | Author | Description of Changes |
|---|---|---|---|
| 1.0 | 2025-10-02 | Architecture Team | Initial design document |
The Python-SLAM system follows a modular, layered architecture with the following design principles:
- Separation of Concerns: Clear boundaries between SLAM processing, visualization, acceleration, and integration
- Platform Abstraction: Hardware and OS-specific optimizations isolated in dedicated modules
- Performance First: Critical path optimizations with fallback mechanisms for compatibility
- Extensibility: Plugin-based architecture for algorithms and hardware acceleration
- Production Ready: Enterprise-grade error handling, monitoring, and configuration management
Primary Pattern: Layered Architecture with Plugin Components
graph TB
subgraph "Presentation Layer"
GUI[GUI Applications]
CLI[CLI Interface]
API[Python API]
end
subgraph "Application Layer"
CTRL[Application Controllers]
WORK[Workflow Orchestration]
CONF[Configuration Management]
end
subgraph "Domain Layer"
SLAM[SLAM Core Logic]
VIZ[Visualization Engine]
BENCH[Benchmarking Engine]
end
subgraph "Infrastructure Layer"
GPU[GPU Acceleration]
ROS2[ROS2 Integration]
ARM[Embedded Optimization]
IO[I/O Management]
end
subgraph "Platform Layer"
OS[Operating System]
HW[Hardware Abstraction]
LIBS[Third-party Libraries]
end
GUI --> CTRL
CLI --> CTRL
API --> CTRL
CTRL --> SLAM
CTRL --> VIZ
CTRL --> BENCH
SLAM --> GPU
SLAM --> ARM
VIZ --> GPU
BENCH --> IO
GPU --> HW
ROS2 --> OS
ARM --> HW
Constraints:
- Python 3.9+ required for type hints and performance improvements
- Real-time processing requires <100ms latency
- Memory usage must remain <512MB on desktop, <256MB on embedded
- GPU acceleration optional but recommended for performance
- Cross-platform compatibility (Linux, Windows, macOS, ARM)
Assumptions:
- Input cameras provide synchronized stereo or RGB-D data
- System has access to at least 4 CPU cores for parallel processing
- Graphics hardware supports OpenGL 3.3+ for visualization
- Network connectivity available for ROS2 integration (when used)
graph LR
subgraph "External Systems"
CAM[Camera Hardware]
ROS[ROS2 Ecosystem]
GPU_HW[GPU Hardware]
DATASETS[SLAM Datasets]
end
subgraph "Python-SLAM System"
CORE[SLAM Core Engine]
GUI_SYS[GUI System]
ACCEL[Acceleration Layer]
INTEG[Integration Layer]
end
subgraph "Users"
DEV[Developers]
RESEARCHERS[Researchers]
ROBOT[Robotics Engineers]
end
CAM --> CORE
CORE --> GUI_SYS
CORE --> ACCEL
ACCEL --> GPU_HW
INTEG --> ROS
DATASETS --> CORE
DEV --> GUI_SYS
RESEARCHERS --> GUI_SYS
ROBOT --> INTEG
graph TB
subgraph "User Interface Components"
MW[Main Window]
VIZ3D[3D Visualization]
CTRL_PANEL[Control Panels]
METRICS[Metrics Dashboard]
end
subgraph "Core SLAM Components"
PIPELINE[SLAM Pipeline]
FEATURES[Feature Extraction]
MAPPING[Point Cloud Mapping]
LOOP[Loop Closure]
OPTIM[Pose Optimization]
end
subgraph "Acceleration Components"
GPU_MGR[GPU Manager]
CUDA_ACC[CUDA Acceleration]
ROCM_ACC[ROCm Acceleration]
METAL_ACC[Metal Acceleration]
ARM_OPT[ARM Optimization]
end
subgraph "Integration Components"
ROS2_BRIDGE[ROS2 Nav2 Bridge]
BENCHMARK[Benchmark Runner]
CONFIG[Configuration Manager]
API_LAYER[API Layer]
end
MW --> PIPELINE
VIZ3D --> MAPPING
CTRL_PANEL --> CONFIG
METRICS --> BENCHMARK
PIPELINE --> FEATURES
PIPELINE --> MAPPING
PIPELINE --> LOOP
PIPELINE --> OPTIM
FEATURES --> GPU_MGR
MAPPING --> ARM_OPT
GPU_MGR --> CUDA_ACC
GPU_MGR --> ROCM_ACC
GPU_MGR --> METAL_ACC
PIPELINE --> ROS2_BRIDGE
OPTIM --> BENCHMARK
CONFIG --> API_LAYER
| Technology | Purpose | Rationale | Alternatives Considered | Selection Criteria |
|---|---|---|---|---|
| Python 3.9+ | Core Language | • Rapid development • Extensive ML/CV libraries • Cross-platform compatibility • Strong community support |
C++, Java, Rust | Development velocity, library ecosystem, maintainability |
| PyQt6/PySide6 | GUI Framework | • Professional desktop UI capabilities • 3D visualization support • Cross-platform native look • OpenGL integration |
Tkinter, wxPython, Kivy | Professional appearance, 3D support, platform integration |
| OpenCV | Computer Vision | • Industry standard CV library • Optimized algorithms • Hardware acceleration support • Extensive feature set |
PIL/Pillow, scikit-image | Performance, feature completeness, hardware optimization |
| NumPy | Numerical Computing | • High-performance array operations • SIMD optimization • Memory efficiency • Ecosystem compatibility |
Pure Python, JAX | Performance, ecosystem integration, stability |
| OpenGL/VTK | 3D Visualization | • Hardware-accelerated rendering • Cross-platform graphics • Scientific visualization • Real-time performance |
Matplotlib 3D, Three.js | Hardware acceleration, real-time performance, quality |
| CUDA/ROCm/Metal | GPU Acceleration | • Multi-vendor GPU support • Massive parallel processing • Memory bandwidth utilization • Vendor optimizations |
OpenCL, CPU-only | Performance, vendor support, optimization quality |
| ROS2 | Robotics Integration | • Modern robotics standard • Real-time capabilities • Distributed architecture • Quality of Service controls |
ROS1, custom protocols | Industry adoption, real-time support, ecosystem |
| ARM NEON | Embedded Optimization | • Hardware SIMD acceleration • Low power consumption • ARM-specific optimizations • Register-level control |
Generic SIMD, CPU-only | Performance on target hardware, power efficiency |
flowchart TD
INPUT[Camera Input<br/>Stereo/RGB-D] --> CALIB[Camera Calibration<br/>& Synchronization]
CALIB --> PREPROC[Image Preprocessing<br/>Rectification, Filtering]
PREPROC --> FEATURE_EXT[Feature Extraction<br/>ORB/SIFT/SURF]
FEATURE_EXT --> TRACKING[Feature Tracking<br/>Optical Flow]
TRACKING --> STEREO[Stereo Matching<br/>Depth Estimation]
STEREO --> POSE_EST[Pose Estimation<br/>PnP/ICP]
POSE_EST --> MAPPING[Point Cloud Mapping<br/>3D Reconstruction]
MAPPING --> LOOP_DET[Loop Detection<br/>Place Recognition]
LOOP_DET --> OPTIM[Global Optimization<br/>Bundle Adjustment]
OPTIM --> OUTPUT[Pose Output<br/>Map Update]
OUTPUT --> VIZ[3D Visualization]
OUTPUT --> ROS2_PUB[ROS2 Publishing]
OUTPUT --> METRICS[Performance Metrics]
style INPUT fill:#e1f5fe
style OUTPUT fill:#e8f5e8
style VIZ fill:#fff3e0
style ROS2_PUB fill:#f3e5f5
style METRICS fill:#fce4ec
flowchart LR
CPU_DATA[CPU Data<br/>Images, Features] --> GPU_TRANSFER[GPU Transfer<br/>Memory Copy]
GPU_TRANSFER --> GPU_PROC[GPU Processing<br/>Parallel Kernels]
GPU_PROC --> GPU_SYNC[GPU Synchronization<br/>Kernel Completion]
GPU_SYNC --> CPU_TRANSFER[CPU Transfer<br/>Results Copy]
CPU_TRANSFER --> CPU_POST[CPU Post-processing<br/>Integration]
subgraph "GPU Backends"
CUDA[CUDA Kernels]
ROCM[ROCm Kernels]
METAL[Metal Kernels]
end
GPU_PROC --> CUDA
GPU_PROC --> ROCM
GPU_PROC --> METAL
style CPU_DATA fill:#e3f2fd
style GPU_PROC fill:#e8f5e8
style CPU_POST fill:#fff3e0
classDiagram
class SLAMPipeline {
-config: SLAMConfig
-feature_extractor: FeatureExtractor
-tracker: FeatureTracker
-mapper: PointCloudMapper
-loop_detector: LoopDetector
-optimizer: PoseOptimizer
-performance_monitor: PerformanceMonitor
+initialize(config: SLAMConfig) bool
+process_frame(image_pair: StereoPair) SLAMResult
+get_current_pose() Pose6DOF
+get_map_points() PointCloud
+save_trajectory(path: str) bool
+get_performance_stats() PerformanceStats
}
class FeatureExtractor {
-detector_type: DetectorType
-max_features: int
-gpu_accelerated: bool
+extract_features(image: ndarray) Tuple[KeyPoints, Descriptors]
+set_detector(detector: DetectorType) None
+configure_gpu(enable: bool) bool
}
class FeatureTracker {
-tracking_method: TrackingMethod
-max_track_length: int
-min_track_quality: float
+track_features(prev_kp: KeyPoints, curr_kp: KeyPoints) Matches
+update_tracks(matches: Matches) TrackingResult
+get_active_tracks() List[Track]
}
class PointCloudMapper {
-map_points: PointCloud
-voxel_size: float
-max_points: int
+add_points(points: ndarray, colors: ndarray) None
+update_map(pose: Pose6DOF, points: ndarray) None
+get_local_map(pose: Pose6DOF, radius: float) PointCloud
+save_map(filename: str) bool
}
SLAMPipeline --> FeatureExtractor
SLAMPipeline --> FeatureTracker
SLAMPipeline --> PointCloudMapper
ORB Feature Extraction (REQ-F-002):
# REQ-F-002: Feature Extraction and Tracking
def extract_orb_features(image: np.ndarray, max_features: int = 1000) -> Tuple[np.ndarray, np.ndarray]:
"""
Extract ORB features from input image.
Fulfills Requirements:
- REQ-F-002: Feature extraction with ORB detector
- REQ-NF-P-001: Real-time performance requirements
Args:
image: Input grayscale image (H, W)
max_features: Maximum number of features to extract
Returns:
keypoints: Array of keypoint coordinates (N, 2)
descriptors: Array of feature descriptors (N, 32)
Performance:
- Target: >500 features from 640x480 image in <10ms
- GPU acceleration available for >3x speedup
"""SIFT Feature Extraction (Alternative algorithm):
# REQ-F-002: Feature Extraction with SIFT algorithm
def extract_sift_features(image: np.ndarray, contrast_threshold: float = 0.04) -> Tuple[np.ndarray, np.ndarray]:
"""
Extract SIFT features with scale and rotation invariance.
Args:
image: Input grayscale image
contrast_threshold: Minimum contrast for feature detection
Returns:
keypoints: Array of keypoint coordinates with scale and orientation
descriptors: Array of 128-dimensional SIFT descriptors
"""classDiagram
class GPUManager {
-available_backends: Dict[str, GPUBackend]
-active_backend: GPUBackend
-fallback_enabled: bool
+initialize_accelerators() bool
+get_best_gpu() GPUInfo
+switch_backend(backend_type: str) bool
+get_accelerator_status() Dict[str, Any]
+benchmark_backends() Dict[str, float]
}
class GPUBackend {
<<abstract>>
+initialize() bool
+is_available() bool
+get_device_info() DeviceInfo
+allocate_memory(size: int) GPUMemory
+execute_kernel(kernel: Kernel, data: Any) Any
+synchronize() None
}
class CUDABackend {
-cuda_device: CUDADevice
-context: CUDAContext
-streams: List[CUDAStream]
+initialize() bool
+create_context() bool
+load_kernel(ptx_code: str) CUDAKernel
+launch_kernel(kernel: CUDAKernel, grid: Tuple, block: Tuple) None
}
class ROCmBackend {
-hip_device: HIPDevice
-context: HIPContext
+initialize() bool
+load_kernel(hsaco_code: str) HIPKernel
+launch_kernel(kernel: HIPKernel, threads: int) None
}
class MetalBackend {
-metal_device: MTLDevice
-command_queue: MTLCommandQueue
+initialize() bool
+load_kernel(metal_code: str) MTLFunction
+execute_compute(function: MTLFunction, data: MTLBuffer) None
}
GPUManager --> GPUBackend
GPUBackend <|-- CUDABackend
GPUBackend <|-- ROCmBackend
GPUBackend <|-- MetalBackend
CUDA Feature Matching Kernel (REQ-F-007, REQ-NF-P-002):
// REQ-F-007: GPU Acceleration Support
// REQ-NF-P-002: GPU Acceleration Performance (>3x speedup)
__global__ void feature_matching_kernel(
const float* descriptors1, // First descriptor set (N1 x 128)
const float* descriptors2, // Second descriptor set (N2 x 128)
int* matches, // Output matches (N1 x 2)
float* distances, // Output distances (N1,)
int n1, int n2, // Number of descriptors
float max_distance // Maximum matching distance
) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx >= n1) return;
float best_dist = FLT_MAX;
float second_best = FLT_MAX;
int best_match = -1;
// Compare with all descriptors in second set
for (int j = 0; j < n2; j++) {
float dist = 0.0f;
// Compute L2 distance between descriptors
for (int k = 0; k < 128; k++) {
float diff = descriptors1[idx * 128 + k] - descriptors2[j * 128 + k];
dist += diff * diff;
}
dist = sqrtf(dist);
if (dist < best_dist) {
second_best = best_dist;
best_dist = dist;
best_match = j;
} else if (dist < second_best) {
second_best = dist;
}
}
// Apply ratio test and distance threshold
if (best_dist < max_distance && best_dist / second_best < 0.8f) {
matches[idx * 2] = idx;
matches[idx * 2 + 1] = best_match;
distances[idx] = best_dist;
} else {
matches[idx * 2] = -1;
matches[idx * 2 + 1] = -1;
distances[idx] = FLT_MAX;
}
}classDiagram
class SlamMainWindow {
-central_widget: QWidget
-menu_bar: QMenuBar
-status_bar: QStatusBar
-dock_widgets: Dict[str, QDockWidget]
-slam_system: SLAMSystem
-update_timer: QTimer
+setup_ui() None
+create_menus() None
+create_dock_widgets() None
+update_visualization() None
+handle_slam_update(result: SLAMResult) None
+show_configuration_dialog() None
}
class Map3DViewer {
-opengl_widget: QOpenGLWidget
-camera: Camera3D
-point_cloud: PointCloudRenderer
-trajectory: TrajectoryRenderer
-shader_program: QOpenGLShaderProgram
+initialize_gl() None
+update_point_cloud(points: PointCloud) None
+update_trajectory(poses: List[Pose6DOF]) None
+set_camera_pose(pose: Pose6DOF) None
+render_frame() None
}
class ControlPanel {
-slam_controls: SLAMControlWidget
-camera_controls: CameraControlWidget
-recording_controls: RecordingControlWidget
+setup_slam_controls() None
+setup_camera_controls() None
+connect_signals() None
+update_status(status: SystemStatus) None
}
class MetricsDashboard {
-fps_chart: QChart
-accuracy_chart: QChart
-memory_chart: QChart
-performance_table: QTableWidget
+update_fps(fps: float) None
+update_accuracy(ate: float, rpe: float) None
+update_memory(usage: MemoryInfo) None
+add_performance_sample(sample: PerformanceSample) None
}
SlamMainWindow --> Map3DViewer
SlamMainWindow --> ControlPanel
SlamMainWindow --> MetricsDashboard
OpenGL Rendering Pipeline (REQ-F-005, REQ-F-012):
# REQ-F-005: Multi-Platform GUI Application
# REQ-F-012: Visualization and Monitoring
class PointCloudRenderer:
"""
High-performance point cloud rendering using OpenGL.
Fulfills Requirements:
- REQ-F-012: Real-time 3D visualization (>20 FPS)
- REQ-F-005: Cross-platform GUI support
- REQ-NF-P-001: Performance requirements
"""
def __init__(self):
self.vertex_buffer = None
self.color_buffer = None
self.point_count = 0
self.shader_program = None
def initialize_gl(self):
"""Initialize OpenGL resources and shaders."""
# Vertex shader for point cloud rendering
vertex_shader_source = """
#version 330 core
layout (location = 0) in vec3 position;
layout (location = 1) in vec3 color;
uniform mat4 mvp_matrix;
uniform float point_size;
out vec3 vertex_color;
void main() {
gl_Position = mvp_matrix * vec4(position, 1.0);
gl_PointSize = point_size;
vertex_color = color;
}
"""
# Fragment shader for point rendering
fragment_shader_source = """
#version 330 core
in vec3 vertex_color;
out vec4 fragment_color;
void main() {
vec2 coord = gl_PointCoord - vec2(0.5);
if (dot(coord, coord) > 0.25) discard;
fragment_color = vec4(vertex_color, 1.0);
}
"""classDiagram
class Nav2Bridge {
-node: rclpy.Node
-pose_publisher: Publisher
-map_publisher: Publisher
-tf_broadcaster: TransformBroadcaster
-camera_subscriber: Subscription
-slam_system: SLAMSystem
+initialize_ros2() bool
+publish_pose(pose: Pose6DOF) None
+publish_map(point_cloud: PointCloud) None
+handle_camera_data(msg: Image) None
+broadcast_transform(pose: Pose6DOF) None
}
class ROS2MessageHandler {
+pose_to_odometry(pose: Pose6DOF) Odometry
+pointcloud_to_ros(points: PointCloud) PointCloud2
+image_from_ros(msg: Image) np.ndarray
+synchronize_stereo(left: Image, right: Image) StereoPair
}
class Nav2Status {
+is_connected: bool
+last_pose_time: Time
+message_counts: Dict[str, int]
+error_status: str
+update_connection_status() None
+get_status_summary() Dict[str, Any]
}
Nav2Bridge --> ROS2MessageHandler
Nav2Bridge --> Nav2Status
Core SLAM API (REQ-I-001):
# REQ-I-001: Python API Interface
class PythonSLAMAPI:
"""
Main API interface for Python-SLAM system.
Fulfills Requirements:
- REQ-I-001: Comprehensive Python API
- REQ-F-010: Configuration management
"""
def __init__(self, config_path: Optional[str] = None):
"""Initialize SLAM system with optional configuration."""
def initialize(self) -> bool:
"""Initialize all system components."""
def process_stereo_frame(self,
left_image: np.ndarray,
right_image: np.ndarray,
timestamp: float) -> SLAMResult:
"""Process stereo camera frame and return SLAM results."""
def get_current_pose(self) -> Pose6DOF:
"""Get current camera/robot pose estimate."""
def get_map_points(self) -> PointCloud:
"""Get current point cloud map."""
def save_trajectory(self, filepath: str, format: str = "TUM") -> bool:
"""Save trajectory in specified format."""
def start_recording(self, filepath: str) -> bool:
"""Start recording sensor data."""
def stop_recording(self) -> bool:
"""Stop recording sensor data."""
def run_benchmark(self, dataset_path: str) -> BenchmarkResults:
"""Run benchmark evaluation on dataset."""Configuration Schema (REQ-I-003):
# REQ-I-003: Configuration File Interface
# REQ-F-010: Configuration Management
@dataclass
class SLAMConfig:
"""
Complete SLAM system configuration.
Fulfills Requirements:
- REQ-F-010: Comprehensive configuration management
- REQ-I-003: JSON/YAML configuration support
"""
# Camera Configuration
camera: CameraConfig = field(default_factory=CameraConfig)
# Feature Extraction
features: FeatureConfig = field(default_factory=FeatureConfig)
# GPU Acceleration
gpu: GPUConfig = field(default_factory=GPUConfig)
# Performance Settings
performance: PerformanceConfig = field(default_factory=PerformanceConfig)
# ROS2 Integration
ros2: ROS2Config = field(default_factory=ROS2Config)
# Embedded Optimization
embedded: EmbeddedConfig = field(default_factory=EmbeddedConfig)
def validate(self) -> List[str]:
"""Validate configuration and return list of errors."""
def save_to_file(self, filepath: str) -> bool:
"""Save configuration to JSON/YAML file."""
@classmethod
def load_from_file(cls, filepath: str) -> 'SLAMConfig':
"""Load configuration from JSON/YAML file."""Pose Publishing:
# REQ-I-002: ROS2 Message Interface
def publish_pose_estimate(pose: Pose6DOF, timestamp: float) -> None:
"""
Publish pose estimate as nav_msgs/Odometry.
Fulfills Requirements:
- REQ-I-002: Standard ROS2 message interfaces
- REQ-F-008: ROS2 Nav2 integration
"""
odometry_msg = Odometry()
odometry_msg.header.stamp = rclpy.time.Time(seconds=timestamp).to_msg()
odometry_msg.header.frame_id = "odom"
odometry_msg.child_frame_id = "base_link"
# Position
odometry_msg.pose.pose.position.x = pose.translation[0]
odometry_msg.pose.pose.position.y = pose.translation[1]
odometry_msg.pose.pose.position.z = pose.translation[2]
# Orientation (quaternion)
odometry_msg.pose.pose.orientation.x = pose.quaternion[0]
odometry_msg.pose.pose.orientation.y = pose.quaternion[1]
odometry_msg.pose.pose.orientation.z = pose.quaternion[2]
odometry_msg.pose.pose.orientation.w = pose.quaternion[3]
# Covariance matrix (6x6)
odometry_msg.pose.covariance = pose.covariance.flatten().tolist()Point Cloud Publishing:
# REQ-I-002: ROS2 Message Interface
def publish_point_cloud(points: PointCloud, timestamp: float) -> None:
"""
Publish point cloud as sensor_msgs/PointCloud2.
Fulfills Requirements:
- REQ-I-002: PointCloud2 message publishing
- REQ-F-003: 3D mapping and reconstruction
"""
cloud_msg = PointCloud2()
cloud_msg.header.stamp = rclpy.time.Time(seconds=timestamp).to_msg()
cloud_msg.header.frame_id = "map"
# Point cloud fields
cloud_msg.fields = [
PointField(name="x", offset=0, datatype=PointField.FLOAT32, count=1),
PointField(name="y", offset=4, datatype=PointField.FLOAT32, count=1),
PointField(name="z", offset=8, datatype=PointField.FLOAT32, count=1),
PointField(name="rgb", offset=12, datatype=PointField.UINT32, count=1),
]
cloud_msg.point_step = 16 # 4 bytes per field
cloud_msg.row_step = cloud_msg.point_step * len(points.positions)
cloud_msg.height = 1
cloud_msg.width = len(points.positions)
cloud_msg.is_dense = True
# Pack point data
cloud_msg.data = pack_pointcloud_data(points.positions, points.colors)# Core SLAM data types for all system components
@dataclass
class Pose6DOF:
"""6-DOF pose representation with uncertainty."""
translation: np.ndarray # 3D position [x, y, z]
quaternion: np.ndarray # Orientation [qx, qy, qz, qw]
covariance: np.ndarray # 6x6 uncertainty matrix
timestamp: float # Pose timestamp
def to_matrix(self) -> np.ndarray:
"""Convert to 4x4 transformation matrix."""
def inverse(self) -> 'Pose6DOF':
"""Compute inverse pose."""
@dataclass
class PointCloud:
"""3D point cloud with color information."""
positions: np.ndarray # Nx3 point positions
colors: np.ndarray # Nx3 RGB colors [0-1]
normals: Optional[np.ndarray] = None # Nx3 surface normals
timestamps: Optional[np.ndarray] = None # N timestamps
def downsample(self, voxel_size: float) -> 'PointCloud':
"""Downsample point cloud using voxel grid."""
@dataclass
class SLAMResult:
"""Complete SLAM processing result."""
pose: Pose6DOF
new_map_points: PointCloud
loop_closure_detected: bool
processing_time: float
feature_count: int
tracking_quality: float@dataclass
class PerformanceSample:
"""Single performance measurement sample."""
timestamp: float
fps: float
memory_usage_mb: float
cpu_usage_percent: float
gpu_usage_percent: float
feature_count: int
processing_time_ms: float
@dataclass
class BenchmarkResults:
"""Complete benchmark evaluation results."""
ate_rmse: float # Absolute Trajectory Error
rpe_rmse: float # Relative Pose Error
processing_fps: float # Average processing rate
memory_peak_mb: float # Peak memory usage
success_rate: float # Tracking success rate
detailed_results: Dict[str, Any] # Detailed metricsAlgorithm Flow:
- FAST Corner Detection: Identify corner points using FAST algorithm
- Harris Corner Response: Compute corner response for each FAST point
- Orientation Assignment: Compute dominant orientation using intensity centroid
- Descriptor Computation: Generate 256-bit binary descriptor using rotated BRIEF
- Feature Filtering: Apply quality thresholds and non-maximum suppression
Performance Characteristics:
- Target: 500+ features from 640x480 image in <10ms (CPU)
- GPU Acceleration: 3-5x speedup with parallel processing
- Memory Usage: ~1MB for 1000 features with descriptors
Algorithm Flow:
- Scale-Space Construction: Build Gaussian pyramid with octaves and scales
- DoG Computation: Compute Difference of Gaussians for keypoint detection
- Extrema Detection: Find local maxima/minima in DoG scale space
- Keypoint Refinement: Sub-pixel localization and edge response filtering
- Orientation Assignment: Compute dominant orientations from gradient histogram
- Descriptor Computation: Generate 128-dimensional SIFT descriptor
Performance Characteristics:
- Target: 300+ features from 640x480 image in <50ms (CPU)
- Memory Usage: ~5MB for 1000 features with descriptors
- Quality: Superior invariance to scale, rotation, and illumination changes
def estimate_pose_pnp(object_points_3d: np.ndarray,
image_points_2d: np.ndarray,
camera_matrix: np.ndarray,
prev_pose: Optional[Pose6DOF] = None) -> Pose6DOF:
"""
Estimate camera pose using Perspective-n-Point algorithm.
Fulfills Requirements:
- REQ-F-001: Real-time SLAM processing
- REQ-NF-P-001: <100ms latency requirement
Args:
object_points_3d: 3D points in world coordinates (Nx3)
image_points_2d: Corresponding 2D image points (Nx2)
camera_matrix: Camera intrinsic matrix (3x3)
prev_pose: Previous pose for initialization (optional)
Returns:
Estimated 6-DOF camera pose with uncertainty
Algorithm:
1. RANSAC outlier rejection with adaptive threshold
2. EPnP algorithm for initial pose estimation
3. Levenberg-Marquardt refinement
4. Uncertainty estimation from residual analysis
"""Algorithm Components:
- Visual Vocabulary: Pre-trained vocabulary of visual words from training data
- Bag-of-Words Representation: Convert image features to BoW histogram
- Similarity Computation: Compute similarity using normalized dot product
- Temporal Consistency: Require multiple consecutive similar frames
- Geometric Verification: Verify loop closure using geometric constraints
Performance Requirements:
- Detection Rate: >95% true positive rate for actual loop closures
- False Positive Rate: <1% false positives to avoid mapping errors
- Processing Time: <100ms per frame for place recognition
| Component | Target Performance | Measurement Method |
|---|---|---|
| Feature Extraction | 500+ features in <10ms | Timing benchmarks |
| Pose Estimation | <20ms per frame | Real-time profiling |
| Point Cloud Update | 1000+ points in <5ms | Memory and timing |
| GUI Rendering | >20 FPS for 3D visualization | Frame rate monitoring |
| GPU Acceleration | >3x speedup vs CPU | Comparative benchmarks |
| Memory Usage | <512MB desktop, <256MB embedded | Memory profiling |
- SIMD Vectorization: Use NumPy vectorized operations and explicit SIMD
- Memory Layout: Optimize data structures for cache efficiency
- Parallel Processing: Multi-threading for independent operations
- Algorithmic Optimization: Choose algorithms based on performance characteristics
- Memory Coalescing: Ensure efficient GPU memory access patterns
- Occupancy Optimization: Maximize GPU core utilization
- Memory Management: Minimize GPU-CPU memory transfers
- Kernel Fusion: Combine operations to reduce kernel launch overhead
- ARM NEON SIMD: Use ARM-specific SIMD instructions
- Fixed-Point Arithmetic: Replace floating-point with fixed-point where possible
- Power Management: Adaptive quality settings based on thermal/power constraints
- Memory Optimization: Minimize memory allocations and fragmentation
Image Data Validation (REQ-NF-S-001):
def validate_image_input(image: np.ndarray) -> bool:
"""
Validate input image data for security and safety.
Fulfills Requirements:
- REQ-NF-S-001: Input validation for security
- REQ-NF-R-002: Data integrity assurance
"""
# Check image dimensions
if len(image.shape) not in [2, 3]:
raise ValueError("Invalid image dimensions")
# Check image size limits
max_pixels = 10_000_000 # 10MP limit
if image.size > max_pixels:
raise ValueError("Image too large")
# Check data type
if image.dtype not in [np.uint8, np.uint16, np.float32]:
raise ValueError("Unsupported image data type")
# Check for valid pixel values
if image.dtype == np.uint8:
if not (0 <= image.min() and image.max() <= 255):
raise ValueError("Invalid pixel values for uint8")
return TrueGraceful Degradation (REQ-NF-R-003):
- Feature Extraction Failure: Fall back to simpler feature detector
- GPU Unavailable: Automatic fallback to CPU processing
- Tracking Loss: Re-initialization with expanded search window
- Memory Pressure: Adaptive quality reduction and cleanup
- Network Failure: Continue operation without ROS2 publishing
Document End
This Software Design Document is maintained as part of the Python-SLAM configuration management system. All design changes must be approved through the established change control process.