Loop Detection and Correction of 3D Laser-Based SLAM with Visual Information

Three-dimensional (3D) laser-based simultaneous localization and mapping (SLAM) can provide real-time pose information and construct accurate 3D map. However, detecting loop closures is a challenge in the 3D laser-based SLAM for expensive computation of algorithms. In this paper, we propose a visual method to detect and correct loop closures. We introduce visual bags-of-words techniques for loop closure detection in the 3D laser-based SLAM. Time of computing similarities between points clouds can be saved. Our method maintains visual keyframes, each of which associates with its pose and segmentation of laser point clouds. Our experiments on KITTI dataset prove that our method can efficiently reduce motion accumulation errors and successfully ensure the real-time performance of loop closure correction.