Robots use 3D mapping to create real-time spatial models of their environment, enabling them to navigate autonomously and detect objects. Sensors like LiDAR (Light Detection and Ranging), stereo cameras, or depth sensors (e.g., Intel RealSense) capture distance and geometry data, which is processed into a 3D point cloud or voxel grid. Algorithms such as SLAM (Simultaneous Localization and Mapping) combine sensor input with motion data to build the map while tracking the robot’s position within it. For example, a warehouse robot might use LiDAR to scan shelves and aisles, generating a 3D model that includes obstacles like pallets or uneven floors. This map serves as the foundation for both navigation and object detection.
For navigation, robots analyze the 3D map to plan collision-free paths. Pathfinding algorithms like A* or RRT (Rapidly-exploring Random Tree) calculate routes using the map’s geometry, while real-time updates from sensors help avoid dynamic obstacles. For instance, a delivery robot navigating a busy hospital corridor might adjust its path when detecting moving people or equipment by comparing live sensor data against the static map. Depth information from 3D mapping also allows robots to distinguish between fixed structures (e.g., walls) and temporary obstructions (e.g., chairs), improving decision-making in complex environments.
In object detection, 3D data provides precise size, shape, and spatial context. Machine learning models trained on 3D datasets can classify objects by matching point cloud segments to known patterns. A robotic arm in a factory might identify tools on a workbench by analyzing their 3D contours, even if lighting conditions or occlusions confuse 2D cameras. Techniques like voxel-based CNNs or point cloud libraries (e.g., PointNet) process this data efficiently. For example, an agricultural robot using stereo cameras could detect and avoid irregularly shaped rocks in a field by analyzing elevation changes in the 3D map, ensuring safe navigation while performing tasks.