KISS-SLAM：一种具有增强泛化能力的简单、鲁棒且精确的3D LiDAR SLAM系统
KISS-SLAM: A Simple, Robust, and Accurate 3D LiDAR SLAM System With Enhanced Generalization Capabilities
AI总结：
本文提出KISS-SLAM，一个遵循KISS原则的仅用LiDAR SLAM系统，通过LiDAR里程计、局部地图匹配与位姿图优化实现高精度、强泛化和实时运行。
摘要：
利用激光扫描仪对环境进行鲁棒且精确的定位与建图，即所谓的LiDAR SLAM，对许多机器人应用至关重要。早期的3D LiDAR SLAM方法通常利用来自IMU或GNSS传感器的额外信息来提高定位精度并减轻漂移。后来，先进系统以更高的运行时间和复杂性为代价进一步改进了估计。本文遵循“保持小巧简单”（KISS）原则，探索仅使用LiDAR的SLAM方法所能达到的极限。通过利用这种极简设计原则，我们的系统KISS-SLAM在位姿精度方面实现了最先进性能，同时在不同环境、传感器和运动模式下部署时几乎不需要参数调整。我们遵循基于图的SLAM的最佳实践，并在LiDAR里程计基础上计算扫描之间的相对运动以及构建环境的局部地图。为了校正漂移，我们匹配局部地图，并在位姿图优化步骤中优化轨迹。实验结果表明，该设计在降低复杂性和对额外传感器模态依赖的同时取得了具有竞争力的性能。通过优先考虑简单性，本工作为仅使用LiDAR的SLAM提供了一个新的强大基线，并为未来研究提供了高性能起点。此外，我们的流程能够构建一致的地图，可直接用于导航等进一步下游任务。我们的开源系统在所有展示的数据集上运行速度均快于传感器帧率，并专为真实世界场景设计。
英文摘要：
Robust and accurate localization and mapping of an environment using laser scanners, so-called LiDAR SLAM, is essential to many robotic applications. Early 3D LiDAR SLAM methods often exploited additional information from IMU or GNSS sensors to enhance localization accuracy and mitigate drift. Later, advanced systems further improved the estimation at the cost of a higher runtime and complexity. This paper explores the limits of what can be achieved with a LiDAR-only SLAM approach while following the "Keep It Small and Simple" (KISS) principle. By leveraging this minimalistic design principle, our system, KISS-SLAM, archives state-of-the-art performances in pose accuracy while requiring little to no parameter tuning for deployment across diverse environments, sensors, and motion profiles. We follow best practices in graph-based SLAM and build upon LiDAR odometry to compute the relative motion between scans and construct local maps of the environment. To correct drift, we match local maps and optimize the trajectory in a pose graph optimization step. The experimental results demonstrate that this design achieves competitive performance while reducing complexity and reliance on additional sensor modalities. By prioritizing simplicity, this work provides a new strong baseline for LiDAR-only SLAM and a high-performing starting point for future research. Further, our pipeline builds consistent maps that can be used directly for further downstream tasks like navigation. Our open-source system operates faster than the sensor frame rate in all presented datasets and is designed for real-world scenarios.
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。
