FAR-LIO：通过快速、准确和鲁棒的LiDAR-惯性里程计实现高速自主
FAR-LIO: Enabling High-Speed Autonomy through Fast, Accurate, and Robust LiDAR-Inertial Odometry
AI总结：
提出FAR-LIO，一种基于CUDA加速的LiDAR-惯性里程计框架，通过新颖的体素哈希映射和稀疏感知GICP算法实现低延迟和高精度，在高速赛车场景中位置误差降低6.9%，运行时间减少38.4%。
摘要：
鲁棒且准确的里程计估计在现代机器人技术中至关重要。在高度动态运动和传感器噪声的环境中，里程计估计变得更具挑战性。自主赛车在非结构化环境中结合了这两个因素，最小化里程计延迟对于稳定的闭环控制至关重要。本文介绍了FAR-LIO，一个高度优化的CUDA加速LiDAR-惯性里程计框架，旨在实现快速、准确和鲁棒的性能。我们的系统利用一种新颖的基于CUDA的体素哈希映射，实现并行化的最近邻搜索和高效的地图更新。我们在基于CUDA的体素哈希映射上采用自适应密度的稀疏感知广义迭代最近点算法，并结合自适应阈值，以实现低延迟而不牺牲精度。扩展卡尔曼滤波器作为鲁棒的后端，利用上采样和延迟补偿策略将LiDAR里程计与高频IMU数据融合，从而确保鲁棒且平滑的里程计输出。我们在四种不同的传感器设置上评估FAR-LIO，使用公共数据集和两辆时速高达250公里的自主赛车的实际数据。与使用单一参数集的目标硬件上的最先进基线相比，FAR-LIO实现了平均位置误差降低6.9%，运行时间减少38.4%。这证明了其计算效率和广泛适用性。为促进后续研究，我们的代码已在以下网址开源：https://github.com/example/FAR-LIO。
英文摘要：
Robust and accurate odometry estimation is essential in modern robotics. In environments characterized by highly dynamic motion and sensor noise, odometry estimation becomes increasingly challenging. Autonomous racing combines both factors in an unstructured setting, where minimizing odometry latency is essential for stable closed-loop control. This paper introduces FAR-LIO, a highly optimized CUDA-accelerated LiDAR-inertial odometry framework developed for Fast, Accurate, and Robust performance. Our system leverages a novel CUDA-based voxel hashmap to enable parallelized nearest-neighbor search and efficient map updates. We employ a sparsity-aware Generalized Iterative Closest Point algorithm with adaptive thresholding on top of the CUDA-based voxel hashmap with adaptive density to achieve low-latency without compromising accuracy. An Extended Kalman Filter serves as a robust backend. It utilizes an upsampling and delay compensation strategy to fuse the LiDAR odometry with high-frequency IMU data, thereby ensuring a robust and smooth odometry output. We evaluate FAR-LIO across four different sensor setups, using both public datasets and data from two autonomous racecars driving at speeds of up to 250 km/h. FAR-LIO achieves an average 6.9% reduction in the positional error and 38.4% lower runtime compared to state-of-the-art baselines on target hardware using a single parameter set. This demonstrates its computational efficiency and broad applicability. To build upon our work, our code is available open-source on https://github.com/TUMFTM/FAR-LIO.
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。
