3D mapping of outdoor environments by scan matching and motion averaging

Abstract

Building 3D maps of surroundings is a fundamental and important task for autonomous robots operating in large previously unknown semi-structured or unstructured outdoor environments, where external methods for localization like GPS are not always available. Most of existing 3D mapping approaches are based on the local scan matching algorithm, which are difficult to achieve accurate mapping with good efficiency. In this paper, we propose an effective graph-based SLAM approach for 3D mapping of outdoor environments without odometry measurement. It first utilizes scan matching algorithm to sequentially estimate initial robot poses and progressively construct a pose graph, where each vertex denotes the robot pose at a point in time and each edge indicates the constraint between two robot poses. As scan matching algorithm cannot be error-free, the estimation of robot poses suffers from the error accumulation problem. To address this issue, the hypothesis of loop closure can be generated by an adaptive search circle and then verified by the global scan matching algorithm. Once the loop closure is verified, more edges should be added into the pose graph, which is subsequently optimized by the weighted motion averaging algorithm so as to obtain accurate robot poses for 3D mapping. Experimental results carried out on multiple outdoor environment data sets show that the proposed approach can build 3D maps with good performance.