SLAM

SLAM (Simultaneous Localization and Mapping) is a computational problem in robotics and computer vision where a device builds a map of an unknown environment while simultaneously tracking its own location within that map. For operators running local AI, SLAM appears in applications like autonomous navigation, AR/VR, and drone flight. The runtime must process sensor data (e.g., camera frames, LiDAR scans) in real-time to update both the map and the pose estimate. SLAM algorithms typically run on-device to avoid latency, making GPU or NPU acceleration relevant for real-time performance.

SLAM algorithms solve a chicken-and-egg problem: to localize, you need a map; to build a map, you need to know your location. Early approaches used extended Kalman filters (EKF-SLAM) or particle filters (FastSLAM). Modern methods leverage visual SLAM (e.g., ORB-SLAM3, DSO) using camera features, or LiDAR SLAM (e.g., LOAM, Cartographer) using point clouds. Deep learning variants (e.g., DROID-SLAM, DPV-SLAM) use neural networks for feature matching or end-to-end pose estimation, which can benefit from GPU inference. For local AI operators, running a SLAM system on a Jetson or laptop GPU requires balancing model size (e.g., lightweight vs. heavy neural backends) and frame rate (typically 10-30 FPS). Quantization or TensorRT optimization may be needed to meet real-time constraints.

Consider running ORB-SLAM3 on an NVIDIA Jetson Orin NX (16 GB RAM, 20 TOPS). With a standard camera at 30 FPS, the system processes each frame to extract ORB features and match them against the map. If the map grows large (e.g., 10,000+ keyframes), the optimization backend (g2o) may cause frame drops. Operators can reduce the keyframe insertion rate or lower the feature count to maintain real-time performance.

In a local AI pipeline for autonomous drone navigation, you might use a SLAM library like ORB-SLAM3 or RTAB-Map. The workflow: capture camera frames → run feature extraction (CPU/GPU) → estimate pose → update map → publish odometry. In ROS2, you'd launch the SLAM node with parameters like orb_slam3/config/stereo.yaml. If using a deep learning SLAM like DROID-SLAM, you'd load a PyTorch model onto the GPU and run inference at 10-15 FPS on an RTX 3060. Monitor VRAM usage; a 6 GB card may struggle with high-resolution inputs.