This study introduces a novel framework, G3Reg, for fast and robust global registration of LiDAR point clouds. In contrast to conventional complex keypoints and descriptors, we extract fundamental geometric primitives, including planes, clusters, and lines (PCL) from the raw point cloud to obtain low-level semantic segments. Each segment is represented as a unified Gaussian Ellipsoid Model (GEM), using a probability ellipsoid to ensure the ground truth centers are encompassed with a certain degree of probability. Utilizing these GEMs, we present a distrust-and-verify scheme based on a Pyramid Compatibility Graph for Global Registration (PAGOR). Specifically, we establish an upper bound, which can be traversed based on the confidence level for compatibility testing to construct the pyramid graph. Then, we solve multiple maximum cliques (MAC) for each level of the pyramid graph, thus generating the corresponding transformation candidates. In the verification phase, we adopt a precise and efficient metric for point cloud alignment quality, founded on geometric primitives, to identify the optimal candidate. The algorithm’s performance is validated on three publicly available datasets and a self-collected multi-session dataset. Parameter settings remained unchanged during the experiment evaluations. The results exhibit superior robustness and real-time performance of the G3Reg framework compared to state-of-the-art methods. Furthermore, we demonstrate the potential for integrating individual GEM and PAGOR components into other registration frameworks to enhance their efficacy.Note to Practitioners—Our proposed method aims to perform global registration for outdoor LiDAR point clouds. Our methodology, which extracts point cloud segments and utilizes their centers for registration, differs from conventional approaches that rely on keypoints and descriptors. We further propose GEM to model the uncertainty of the centers and embed it into our distrust-and-verify framework. In theory, our method can be applied to any registration task that involves primitives representable as sets of Gaussians or points. Additionally, practitioners should consider the following to enhance applicability. First, practitioners can fine-tune the parameters of the segmentation algorithm to generate more repeatable segmentation results. Second, although our default setting uses four compatibility test thresholds, fewer may suffice, especially when translations between point clouds are minor. Finally, for geometrically uninformative segments such as vegetation, consider extracting descriptors within these segments to increase correspondences.

This paper proposes a new nonlinear factor sparsification paradigm for general feature-based long-term SLAM backend. Given a pose sparsification policy, we aim to scale the SLAM problem with space explored instead of time in a principled way so that the number of time-indexed poses can be limited. At the same time, their influence and the long-lived landmarks are appropriately maintained. To do this, we propose a new two-step sparsification pipeline. Given a pose node to remove, the first step is performed in the Markov blankets of affected landmarks. It transforms pose-landmark constraints into pose-pose constraints while preserving observability and minimizing information loss in the blanket. Moreover, since landmarks are conditionally independent, we can do this in parallel, disconnecting a pose from all the landmarks. The second step marginalizes the pose of interest with pure pose-wise constraints without affecting landmarks. Our method decouples the management of landmarks from pose-only measurements, making it general for any feature-based SLAM. We also give a practical example of how our backend works by concatenating it to a monocular VIO frontend. In simulation and realworld dataset, our sparsified backend is accurate and efficient. We open-source our backend, along with the VIO+Backend example, to contribute to the community’s betterment.

This paper proposes Contour Context, a simple, effective, and efficient topological loop closure detection pipeline with accurate 3-DoF metric pose estimation, targeting the urban autonomous driving scenario. We interpret the Cartesian bird's eye view (BEV) image projected from 3D LiDAR points as layered distribution of structures. To recover elevation information from BEVs, we slice them at different heights, and connected pixels at each level form contours. Each contour is parameterized by abstract information, e.g., pixel count, center position, covariance, and mean height. The similarity of two BEVs is calculated in sequential discrete and continuous steps. The first step considers the geometric consensus of graph-like constellations formed by contours in particular localities. The second step models the majority of contours as a 2.5D Gaussian mixture model, which is used to calculate correlation and optimize relative transform in continuous space. A retrieval key is designed to accelerate the search of a database indexed by layered KD-trees. We validate the efficacy of our method by comparing it with recent works on public datasets.

This paper proposes a LiDAR-inertial odometry that properly solves the uncertainty estimation problem, guided by the rules of designing a consistent estimator. Our system is built upon an iterated extended Kalman filter, with multiple states in an optimization window. To survive environments without distinctive geometric structures, we do not track features over time. We only extract planar primitives from the local map and use a direct point-to-plane distance metric as the measurement model. The realistic noise parameters are estimated online by modeling point distributions. We use nullspace projection to remove dependency on the feature planes, which is equivalent to transforming the pose-map measurement into relative pose constraints. To avoid reintegrating all the laser points in the local window after every state correction, we use the Schmidt Kalman update to consider the probabilistic effects of past poses while their values are left unaltered. A collection of octrees with an adaptive resolution is designed to manage measurement points and the map efficiently. The consistency and robustness of our system are verified in both simulation and real-world experiments.