Place recognition in 3D data is a challenging task that has been commonly approached by adapting image-based solutions. Methods based on local features suffer from ambiguity and from robustness to environment changes while methods based on global features are viewpoint dependent. We propose SegMatch, a reliable place recognition algorithm based on the matching of 3D segments. Segments provide a good compromise between local and global descriptions, incorporating their strengths while reducing their individual drawbacks. SegMatch does not rely on assumptions of ‘perfect segmentation’, or on the existence of ‘objects’ in the environment, which allows for reliable execution on large scale, unstructured environments. We quantitatively demonstrate that SegMatch can achieve accurate localization at a frequency of 1Hz on the largest sequence of the KITTI odometry dataset. We furthermore show how this algorithm can reliably detect and close loops in real-time, during online operation. In addition, the source code for the SegMatch algorithm is made publicly available1.
While local keypoint features often lack descriptive power, global descriptors can struggle with invariance. Therefore other works have also proposed to use 3D segments or objects for the place recognition task. , for example, propose to perform place recognition by detecting planes in 3D environments. The planes are accumulated in a graph and an interpretation tree is used to match sub-graphs. A final geometric consistency test is conducted over the planes in the matched sub-graphs. The work is extended in @cite_19 to use the covariance of the plane parameters instead of the number of points in planes for matching. This strategy is only applied to small, indoor environments and assumes a plane model for segments which is no longer valid in unstructured environment. A somewhat analogous, seminal work on object-based loop-closure detection in indoor environments using RGB-D cameras is presented by . Although presenting interesting ideas, their work can only handle a small number of well segmented objects in small scale environments.
@cite_19
2019336290
Most visual simultaneous localization and mapping systems use point features as their landmarks and adopt point-based feature descriptors to recognize them. Compared to point landmarks, however, lines have strength in conveying the structural information of the environment. Despite the benefit, they have not been widely used because lines are more difficult in detecting, tracking, and recognizing, and this delayed the use of lines as landmarks. In this paper, we propose a place recognition algorithm using straight line features, which enables reliable loop closure detections in large complex environments under significant illumination changes. A vocabulary tree trained with mean standard-deviation line descriptor is used in finding the candidate matches between keyframes, and a Bayesian filtering framework enables reliable keyframe matching for large-scale loop closures. The proposed algorithm is compared with state-of-the-art point-based methods using scale-invariant feature transform or speeded up robust features. The experimental results show that the proposed method outperforms the others in challenging indoor environments.