任务驱动的机器人导航SLAM基准测试
Task-driven SLAM Benchmarking For Robot Navigation
AI总结：
针对现有SLAM基准忽视精度的问题，提出任务驱动的TaskSLAM-Bench，以精度为核心指标，结合模拟与真实测试，发现被动立体SLAM在室内精度堪比LiDAR SLAM，并公开代码支持自定义环境测试。
摘要：
移动辅助机器人的SLAM的一个关键用例是支持基于导航的任务中的定位。当前的SLAM基准测试忽视了可重复性（精度）的重要性，尽管其在真实世界部署中具有重要意义。为解决这一差距，我们提出了一种任务驱动的SLAM基准测试方法，即TaskSLAM-Bench。它采用精度作为关键指标，考虑了SLAM的建图能力，并具有易于满足的实现要求。SLAM方法的模拟和真实世界测试场景为现代视觉和LiDAR SLAM解决方案的导航性能特性提供了见解。结果表明，在典型的室内环境中，被动立体SLAM的精度水平可与LiDAR SLAM相媲美。TaskSLAM-Bench补充了现有基准测试，并在以导航为重点的场景中提供了更丰富的SLAM性能评估。公开可用的代码允许在配备适当机器人的自定义环境中进行现场SLAM测试。
英文摘要：
A critical use case of SLAM for mobile assistive robots is to support localization during a navigation-based task. Current SLAM benchmarks overlook the significance of repeatability (precision), despite its importance in real-world deployments. To address this gap, we propose a task-driven approach to SLAM benchmarking, TaskSLAM-Bench. It employs precision as a key metric, accounts for SLAM's mapping capabilities, and has easy-to-meet implementation requirements. Simulated and real-world testing scenarios of SLAM methods provide insights into the navigation performance properties of modern visual and LiDAR SLAM solutions. The outcomes show that passive stereo SLAM operates at a level of precision comparable to LiDAR SLAM in typical indoor environments. TaskSLAM-Bench complements existing benchmarks and offers richer assessment of SLAM performance in navigation-focused scenarios. Publicly available code permits in-situ SLAM testing in custom environments with properly equipped robots.
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