This paper presents Lite VLoc, a hierarchical vi-sual localization framework that uses a lightweight topo-metric map to represent the environment. The method consists of three sequential modules that estimate camera poses in a coarse-to-fine manner. Unlike dense 3D mapping methods, LiteVLoc reduces storage by avoiding geometric reconstruction. It uses a learning-based feature matcher to establish dense correspondences between sparse keyframes and observations, and then refines poses with a geometric solver, enabling robustness to viewpoint changes. The system assumes depth sensors or stereo camera for deployment. A novel dataset for the map-free relocalization task is also introduced. Extensive experiments including localization and navigation in both simulated and real-world scenarios have validate the system's performance and demonstrated its precision and efficiency for large-scale deployment. Code and data will be made publicly available at the webpage:https://rpl-cs-ucl.github.io/LiteVLoc.

Autonomous driving emphasizes precise multi-sensor fusion positioning on limit resource embedded systems. LiDAR-centered sensor fusion system serves as a mainstream navigation system due to its insensitivity to illumination and viewpoint change. However, these types of systems suffer from handling large-scale sequential LiDAR data using limited resources on board, leading LiDAR-centralized sensor fusion unpractical. As a result, hand-crafted features such as plane and edge are leveraged in majority mainstream positioning methods to alleviate this unsatisfaction, triggering a new cornerstone in LiDAR Inertial sensor fusion. However, such super light weight feature extraction, although it achieves real-time constraint in LiDAR-centered sensor fusion, encounters severe vulnerability under high speed rotational or translational perturbation. In this paper, we propose a sparse tensor based LiDAR Inertial fusion method for autonomous driving embedded system. Leveraging the power of sparse tensor, the global geometrical feature is fetched so that the point cloud sparsity defect is alleviated. Inertial sensor is deployed to conquer the time-consuming step caused by the coarse level point-wise inlier matching. We construct our experiments on both representative dataset benchmarks and realistic scenes. The evaluation results show the robustness and accuracy of our proposed solution compared to classical methods.

The generalized rigid registration problem in high-dimensional Euclidean spaces is studied. The loss function is minimized with an equivalent error formulation by the Cayley formula. The closed-form linear least-square solution to such a problem is derived which generates the registration covariances, i.e., uncertainty information of rotation and translation, providing quite accurate probabilistic descriptions. Simulation results indicate the correctness of the proposed method and also present its efficiency on computation-time consumption, compared with previous algorithms using singular value decomposition (SVD) and linear matrix inequality (LMI). The proposed scheme is then applied to an interpolation problem on the special Euclidean group SE(n) with covariance-preserving functionality. Finally, experiments on covariance-aided Lidar mapping show practical superiority in robotic navigation.

Combining multiple sensors enables a robot to maximize its perceptual awareness of environments and enhance its robustness to external disturbance, crucial to robotic navigation. This paper proposes the FusionPortable benchmark, a complete multi-sensor dataset with a diverse set of sequences for mobile robots. This paper presents three contributions. We first advance a portable and versatile multi-sensor suite that offers rich sensory measurements: 10Hz LiDAR point clouds, 20Hz stereo frame images, high-rate and asynchronous events from stereo event cameras, 200Hz inertial readings from an IMU, and 10Hz GPS signal. Sensors are already temporally synchronized in hardware. This device is lightweight, self-contained, and has plug-and-play support for mobile robots. Second, we construct a dataset by collecting 17 sequences that cover a variety of environments on the campus by exploiting multiple robot platforms for data collection. Some sequences are challenging to existing SLAM algorithms. Third, we provide ground truth for the decouple localization and mapping performance evaluation. We additionally evaluate state-of-the-art SLAM approaches and identify their limitations. The dataset, consisting of raw sensor measurements, ground truth, calibration data, and evaluated algorithms, will be released.

Pose estimation problems are fundamental in robotics. Most of these problems are challenging due to the nonconvex nature. This also sets up an obstacle for uncertainty description that is essential for pose integration and quality control. In this article, we show that a large class of related problems can be categorized as the quadratic pose estimation problems (QPEPs) and we propose a general quaternion-based mathematical model to unify these problems. To solve the nonconvex QPEPs, a Gröbner-basis method is investigated to derive their globally optimal and robust solutions. Furthermore, we develop the rules for characterizing the solvability and observability of these solutions. In addition, the uncertainty description, i.e., covariance matrix, as an important piece of information in robotic state estimation frameworks, is analyzed in detail. Theoretical results show that the covariance can be estimated via online optimization, in an efficient and unbiased manner. In this way, both the solution and covariance are guaranteed to be globally optimal. Through simulations and experiments, we show that the proposed QPEP-based solver is not only accurate, robust, and efficient but outperforms the representatives for covariance estimation. The designed algorithms are also assembled as a C++/MATLAB/Octave/ROS library, while these developed interfaces are built for main stream platforms and simultaneous localization and mapping schemes.