Obstacle detection method based on point cloud registration and target reduction processing efficient detection of vehicle obstacles

Abstract

Obstacle detection systems serve as a linchpin in ensuring the safety of autonomous vehicles. While contemporary detection techniques grapple with intricate results, diminished efficiency, and substantial computational burdens, our research introduces a groundbreaking method anchored in point cloud registration and target reduction for areas with obstructions. This approach not only facilitates high-precision positioning but also expedites the process of obstacle detection, a cornerstone for the subsequent navigation and obstacle evasion tasks in intelligent vehicles. Initially, we harness the power of NDT registration to achieve meticulous positioning. Subsequent stages involve refining the detection landscape by filtering out non-road obstacles using our innovative target reduction process and then employing advanced clustering techniques to pinpoint obstacles. We've validated our approach using the comprehensive Mulran dataset. Both qualitative and quantitative assessments underscore the method's robustness, reliability, and superior effectiveness. Experimental outcomes compellingly demonstrate that our newly proposed obstacle detection framework considerably elevates both operational efficiency and accuracy, paving the way for safer and smarter autonomous driving.