LiDAR has gained popularity in autonomous driving due to advantages like long measurement distance, rich three-dimensional information, and stability in harsh environments. Place Recognition (PR) enables vehicles to identify previously visited locations despite variations in appearance, weather, and viewpoints, even determining their global location within prior maps. This capability is crucial for accurate localization in autonomous driving. Consequently, LiDAR-based Place Recognition (LPR) has emerged as a research hotspot in robotics. However, existing reviews predominantly concentrate on Visual Place Recognition, leaving a gap in systematic reviews on LPR. This article bridges this gap by providing a comprehensive review of LPR methods, thus facilitating and encouraging further research. We commence by exploring the relationship between PR and autonomous driving components. Then, we delve into the problem formulation of LPR, challenges, and relations to previous surveys. Subsequently, we conduct an in-depth review of related research, which offers detailed classifications, strengths and weaknesses, and architectures. Finally, we summarize existing datasets and evaluation metrics and envision promising future directions. This article can serve as a valuable tutorial for newcomers entering the field of place recognition. We plan to maintain an up-to-date project on https://github.com/ShiPC-AI/LPR-Survey .

With the development of 3D matching technology, correspondence-based point cloud registration gains more attention. Unfortunately, 3D keypoint techniques inevitably produce a large number of outliers, i.e., outlier rate is often larger than 95%. Guaranteed outlier removal (GORE) Bustos and Chin has shown very good robustness to extreme outliers. However, the high computational cost (exponential in the worst case) largely limits its usages in practice. In this paper, we propose the first $O(N^{2})$ time GORE method, called quadratic-time GORE (QGORE), which preserves the globally optimal solution while largely increases the efficiency. QGORE leverages a simple but effective voting idea via geometric consistency for upper bound estimation, which achieves almost the same tightness as the one in GORE. We also present a one-point RANSAC by exploring “rotation correspondence” for lower bound estimation, which largely reduces the number of iterations of traditional 3-point RANSAC. Further, we propose a l$_{p}$p -like adaptive estimator for optimization. Extensive experiments show that QGORE achieves the same robustness and optimality as GORE while being 1 $\sim$ 2 orders faster. The source code will be made publicly available.

Correspondence-based point cloud registration (PCR) plays a key role in robotics and computer vision. However, challenges like sensor noises, object occlusions, and descriptor limitations inevitably result in numerous outliers. RANSAC family is the most popular outlier removal solution. However, the requisite iterations escalate exponentially with the outlier ratio, rendering it far inferior to existing methods (SC2PCR [1], MAC [2] etc.) in terms of accuracy or speed. Thus, we propose a two-stage consensus filtering (TCF) that elevates RANSAC to state-of-the-art (SOTA) speed and accuracy. Firstly, one-point RANSAC obtains a consensus set based on length consistency. Subsequently, two-point RANSAC refines the set via angle consistency. Then, three-point RANSAC computes a coarse pose and removes outliers based on transformed correspondence's distances. Drawing on optimizations from one-point and two-point RANSAC, three-point RANSAC requires only a few iterations. Eventually, an iterative reweighted least squares (IRLS) is applied to yield the optimal pose. Experiments on the large-scale KITTI and ETH datasets demonstrate our method achieves up to three-orders-of-magnitude speedup compared to MAC while maintaining registration accuracy and recall. Our code is available at https://github.com/ShiPC-AI/TCF .

Herein, we propose a novel indoor structure extraction (ISE) method that can reconstruct an indoor planar structure with a feature structure map (FSM) and enable indoor robot navigation using a navigation structure map (NSM). To construct the FSM, we first propose a two-staged region growing algorithm to segment the planar feature and to obtain the original planar point cloud. Subsequently, we simplify the planar feature using quadtree segmentation based on cluster fusion. Finally, we perform simple triangulation in the interior and vertex-assignment triangulation in the boundary to accomplish feature reconstruction for the planar structure. The FSM is organized in the form of a mesh model. To construct the NSM, we first propose a novel ground extraction method based on indoor structure analysis under the Manhattan world assumption. It can accurately capture the ground plane in an indoor scene. Subsequently, we establish a passable area map (PAM) within different heights. Finally, a novel-form NSM is established using the original planar point cloud and the PAM. Experiments are performed using three public datasets and one self-collected dataset. The proposed plane segmentation approach is evaluated on two simulation datasets and achieves a recall of approximately 99%, which is 5% higher than that of the traditional plane segmentation method. Furthermore, the triangulation performance of our method compared with the traditional greedy projection triangulation show that our method performs better in terms of feature representation. The experimental results reveal that our ISE method is robust and effective for extracting indoor structures.