Local probabilistic terrain estimation for navigation in unstructured environments

Abstract

To enhance the precision and speed of path planning and autonomous navigation for mobile robots in unstructured environments, this paper proposes a method involving multi-sensor data acquisition, fusion, and the local terrain map construction. Utilizing the robot's onboard sensing system, the method achieves real-time elevation map construction and immediate updates centered around the robot's current position during its movement. A sliding window is employed to manage the memory of the map, ensuring real-time performance and robustness. The paper analyzes the influence of error propagation in robot state estimation on the construction of elevation maps and compensates for errors during incremental map updates, addressing the map consistency issue arising from odometry drift. Employing a probability terrain estimation method based on discrete Bayesian filters effectively accumulates measurement values, thus obtaining estimates of occupancy probability in the local environment. This method analyzes and models ground roughness, obstacle distribution, and passable areas in the robot's surroundings, providing valuable prior information for local path planning and obstacle avoidance. Experimental results validate the effectiveness and reliability of the proposed method using real-world data.