Efficient loop closure based on FALKO lidar features for online robot localization and mapping

Abstract

Keypoint features detection from measurements enables efficient localization and map estimation through the compact representation and recognition of locations. The keypoint detector FALKO has been proposed to detect stable points in laser scans for localization and mapping tasks. In this paper, we present novel loop closure methods based on FALKO keypoints and compare their performance in online localization and mapping problems. The pose graph formulation is adopted, where each pose is associated to a local map of keypoints extracted from the corresponding laser scan. Loops in the graph are detected by matching local maps in two steps. First, the candidate matching scans are selected by comparing the scan signatures obtained from the keypoints of each scan. Second, the transformation between two scans is obtained by pairing and aligning the respective keypoint sets. Experiments with standard benchmark datasets assess the performance of FALKO and of the proposed loop closure algorithms in both offline and online localization and map estimation.