面向地面车辆的、具有 SE(2) 约束的多功能激光雷达-惯性里程计
Versatile LiDAR-Inertial Odometry With SE (2) Constraints for Ground Vehicles
AI总结：
本文提出一种紧耦合 LiDAR-惯性 SLAM 框架，将地面车辆位姿参数化于 SE(2)，并把越界运动扰动纳入噪声模型，融合传感器与运动先验以提升室内外定位精度和鲁棒性。
摘要：
自 LiDAR Odometry And Mapping（LOAM）出现以来，LiDAR SLAM 已成为地面车辆的主要定位系统之一。许多关于 LOAM 的扩展工作主要利用某一种特定约束来提升性能，例如来自车载传感器的信息，如回环检测和惯性状态；以及先验条件，如地面平面和运动动力学。在许多机器人应用中，这些条件通常只是部分已知，因此由于存在大量约束，SLAM 系统可能成为一个综合性问题。因此，通过适当地融合这些约束，我们可以获得更好的 SLAM 结果。本文提出一种混合式 LiDAR-惯性 SLAM 框架，该框架同时利用车载感知系统和运动动力学等先验信息来提升定位性能。具体而言，我们考虑地面车辆这一常用于自动驾驶和仓储物流的场景。我们提出一种计算高效的 LiDAR-惯性里程计方法，将地面车辆位姿直接参数化在 SE(2) 上。SE(2) 之外的运动扰动并未被忽略，而是被纳入一个新颖 SE(2) 约束模型的综合噪声项中。在里程计测量处理方面，我们提出一种多功能、紧耦合的 LiDAR-惯性里程计，以实现优于传统 LiDAR 里程计的位姿估计。我们开展了充分实验，在不同场景中评估所提方法的性能，包括室内与室外环境中的定位。所提方法在精度和鲁棒性方面均取得优越性能。
英文摘要：
LiDAR SLAM has become one of the major localization systems for ground vehicles since LiDAR Odometry And Mapping (LOAM). Many extension works on LOAM mainly leverage one specific constraint to improve the performance, e.g., information from on-board sensors such as loop closure and inertial state; prior conditions such as ground level and motion dynamics. In many robotic applications, these conditions are often known partially, hence a SLAM system can be a comprehensive problem due to the existence of numerous constraints. Therefore, we can achieve a better SLAM result by fusing them properly. In this paper, we propose a hybrid LiDAR-inertial SLAM framework that leverages both the on-board perception system and prior information such as motion dynamics to improve localization performance. In particular, we consider the case for ground vehicles, which are commonly used for autonomous driving and warehouse logistics. We present a computationally efficient LiDAR-inertial odometry method that directly parameterizes ground vehicle poses on SE(2). The out-of-SE(2) motion perturbations are not neglected but incorporated into an integrated noise term of a novel SE(2)-constraints model. For odometric measurement processing, we propose a versatile, tightly coupled LiDAR-inertial odometry to achieve better pose estimation than traditional LiDAR odometry. Thorough experiments are performed to evaluate our proposed method's performance in different scenarios, including localization for both indoor and outdoor environments. The proposed method achieves superior performance in accuracy and robustness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
