发表机构
University of Antwerp; Flanders Make Strategic Research Centre（安特卫普大学; 法兰德斯制造战略研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对声纳地点识别模糊性导致地图崩溃的问题，提出BatSLAM 2.0，结合声学前端、序列验证器和因子图位姿图，在模拟与真实数据中实现鲁棒拓扑建图。
AI 中文摘要
回声定位蝙蝠可以利用回声定位在黑暗和杂乱的空间中导航。十多年前，BatSLAM 表明，配备仿生双耳声纳的机器人可以通过识别接收到的声学信号中的地点来构建环境的拓扑地图。然而，声纳地点识别本质上具有模糊性：走廊会产生几乎相同的回声序列，错误的闭环检测可能导致拓扑地图崩溃。在本文中，我们介绍了BatSLAM 2.0，一种新颖的仅声纳SLAM系统，由三个要素构建：更新的声学前端、一个序列验证器（用于跟踪和验证闭环候选）以及一个在高性能因子图框架上实现的位姿图。该系统在模拟和真实世界录音中均进行了全面评估。在两种情况下，BatSLAM 2.0算法都展示了鲁棒拓扑地图创建、对抗地图崩溃以及地图规模鲁棒扩展的能力。
英文摘要
Echolocating bats can navigate dark and cluttered spaces using echolocation. Over a decade ago, BatSLAM showed that a robot with a biomimetic binaural sonar can build a topological map of the environment, by recognizing places from the received acoustic signals. Sonar place recognition, however, is ambiguous by nature: corridors produce nearly identical echo trains, and wrong loop closure can collapse the topological map. In this paper, we introduce BatSLAM 2.0, a novel sonar-only SLAM system built from three elements: an updated acoustic front-end, a sequence verifier that tracks and verifies loop closure candidates and a pose graph implemented on a high performance factor graph framework. The system was thoroughly evaluated both in simulated as well as real world recordings. In both cases, the BatSLAM2.0 algorithm shows the capability of robust topological map creation, countering map collapse, and robust scaling of map size.
