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.

翻译：本文提出直接式激光雷达-惯性里程计与建图（DLIOM），一种鲁棒的SLAM算法，明确聚焦于计算效率、运行可靠性和实际应用效能。DLIOM在前端和后端子系统中包含多项关键算法创新，旨在设计一种对环境具有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确的定位和高保真三维建图。我们的想法源于对现代激光雷达SLAM系统及其在不同运行环境下泛化能力不足的深入探究，通过主动安全防护措施解决了若干常见算法失效点，以在非结构化真实世界中实现长期运行可靠性。我们详述了分布在典型激光雷达SLAM流水线中，对定位精度和建图弹性至关重要的多项创新，从而全面提升算法的速度、精度和鲁棒性。此外，我们讨论了在资源受限系统上实现这种复杂系统进行实时状态估计时，从底层构建方法中获得的见解，并通过实验证明，与当前最先进方法相比，我们的方法在公共基准数据集和自采数据集上均展现出更优性能。