直接激光雷达惯性里程计与建图：感知型与连接型SLAM
Direct LiDAR-Inertial Odometry and Mapping: Perceptive and Connective SLAM
AI总结：
针对现有激光雷达SLAM泛化性差、易失效的问题，本文提出兼具前后端创新的DLIOM算法，通过主动防护提升可靠性，在资源受限系统上实现实时运行，且在公开与自采数据集上性能优于现有最优方法。
摘要：
本文提出了直接激光雷达惯性里程计与建图算法（Direct LiDAR-Inertial Odometry and Mapping, DLIOM），这是一种鲁棒的SLAM算法，明确聚焦于计算效率、运行可靠性与实际场景效能。DLIOM在前端与后端子系统中均包含若干关键算法创新，旨在构建具备环境感知能力的弹性激光雷达-惯性架构，为自主机器人平台提供精准定位与高保真三维建图。我们的思路源于对现代激光雷达SLAM系统的深入调研——这类系统难以在不同运行环境中实现泛化，我们通过主动安全防护机制解决了若干常见的算法失效点，以在非结构化真实世界中保障长期运行可靠性。我们详细阐述了分布于典型激光雷达SLAM流程中、针对定位精度与建图弹性的多项重要创新，以全面提升算法的速度、精度与鲁棒性。此外，我们还讨论了在资源受限系统上实现这类复杂实时状态估计系统时，采用从零构建方法所获得的洞见；并通过实验证明，在公开基准数据集与自采集数据集上，本文方法的性能均优于当前最优水平。
英文摘要：
This paper presents Direct LiDAR-Inertial Odometry and Mapping (DLIOM), a robust SLAM algorithm with an explicit focus on computational efficiency, operational reliability, and real-world efficacy. DLIOM contains several key algorithmic innovations in both the front-end and back-end subsystems to design a resilient LiDAR-inertial architecture that is perceptive to the environment and produces accurate localization and high-fidelity 3D mapping for autonomous robotic platforms. Our ideas spawned after a deep investigation into modern LiDAR SLAM systems and their inabilities to generalize across different operating environments, in which we address several common algorithmic failure points by means of proactive safe-guards to provide long-term operational reliability in the unstructured real world. We detail several important innovations to localization accuracy and mapping resiliency distributed throughout a typical LiDAR SLAM pipeline to comprehensively increase algorithmic speed, accuracy, and robustness. In addition, we discuss insights gained from our ground-up approach while implementing such a complex system for real-time state estimation on resource-constrained systems, and we experimentally show the increased performance of our method as compared to the current state-of-the-art on both public benchmark and self-collected datasets.
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。
