Autonomous Robot Navigation: SLAM, Path Planning & Sensor Fusion

1. SLAM: Simultaneous Localization & Mapping

SLAM solves the chicken-and-egg problem: a robot needs a map to localize, and localization to build a map. Extended Kalman Filter SLAM: represents landmarks as Gaussian distributions, O(n²) per update. Particle filter SLAM (FastSLAM): represents pose as particles, map per particle. Graph-based SLAM: poses and landmarks as graph nodes, constraints as edges — solve via g²o or GTSAM. Visual SLAM: ORB-SLAM3 uses feature-based tracking with ORB descriptors. LSD-SLAM: direct (pixel-intensity) method. Visual-Inertial SLAM: VINS-Mono, Kimera combine camera + IMU. LiDAR SLAM: LOAM, LeGO-LOAM, LIO-SAM. Loop closure detection prevents drift accumulation.

2. Path Planning Algorithms

Graph-based: A* (optimal with admissible heuristic), Dijkstra (uniform cost), D* Lite (replanning). Sampling-based: RRT (Rapidly-exploring Random Trees) for high-dimensional spaces, RRT* (asymptotically optimal), Informed RRT*. Potential fields: attractive (goal) + repulsive (obstacles), local minima problem. Grid-based: Wavefront algorithm, cost maps (occupancy grid + inflation). Dynamic planning: DWA (Dynamic Window Approach), TEB (Timed Elastic Band) for trajectory optimization with dynamic constraints. Coverage planning: boustrophedon decomposition for lawn mowing, floor cleaning. Multi-robot: auction-based task allocation, formation control, conflict-based search (CBS).

3. Sensor Fusion

Sensor modalities: LiDAR (3D point clouds, 10–100m range, ±2cm accuracy), cameras (RGB, stereo, depth), IMU (accelerometer + gyroscope, 100–1000 Hz), wheel encoders, GPS (2–5m accuracy, RTK: 2cm), ultrasonic (short range). Fusion approaches: early fusion (raw data level), feature-level fusion, decision-level fusion. Kalman filter family: EKF (linearized), UKF (unscented transform), particle filter (non-parametric). Factor graph optimization: iSAM2 for incremental smoothing. Deep learning fusion: PointPillars, CenterPoint for LiDAR; BEVFusion for camera-LiDAR. Sensor degradation handling: fog, rain, direct sunlight affect different sensors differently — redundancy is key.

4. Perception & Object Detection

3D object detection: PointNet/PointNet++ process raw point clouds. VoxelNet: voxelization + 3D convolutions. SECOND: sparse convolution acceleration. CenterPoint: anchor-free detection. Camera-based: YOLO, CenterNet for 2D, MonoDEPTH for monocular 3D. Semantic segmentation: cylinder3D for LiDAR, DeepLab for camera. Occupancy networks: predict 3D occupancy grid from sensor data. Tracking: multi-object tracking (MOT) via Hungarian algorithm, SORT/DeepSORT. Prediction: trajectron++ predicts future agent trajectories. HD maps: lane-level maps with semantic information, OpenDRIVE format.

5. Real-World Deployment

Mobile robot platforms: differential drive (iRobot), Ackermann steering (cars), omnidirectional (Mecanum wheels), legged (Boston Dynamics Spot). ROS 2 (Robot Operating System): DDS middleware, lifecycle nodes, Nav2 stack. Navigation stack: sensor drivers → perception → localization → planning → control. Safety: ISO 13482 (personal care robots), ISO 3691-4 (AGVs). Certification: UL 3100 for autonomous mobile robots. Challenges: dynamic environments, long-term autonomy, robust perception in adverse conditions, human-robot interaction. Fleet management: centralized vs. decentralized, task allocation, charging scheduling.

Try it live

Everything above runs in your browser — open Autonomous Robot Navigation Simulator and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.