无需初始化地标位置，基于对称性设计的滤波器显著提升纯测距定位精度。
Equivariant Filter Design for Range-only SLAM
- 利用测量对称性设计不变滤波器，避免地标初始化
- 真实数据集上精度和鲁棒性远超传统EKF方法
- 适合无先验信息的无人机、水下机器人等场景
纯测距同时定位与建图（RO-SLAM）在地面与空中超宽带（UWB）及蓝牙低功耗（BLE）定位，以及水下声学信标定位中具有实际应用价值。本文研究配备惯性测量单元（IMU）和测距传感器的移动机器人，基于与测距测量相容的对称性李群，推导出一种等变滤波器（EqF）。该滤波器无需地标位置的初始值，对无先验情况表现出强鲁棒性。在真实数据集上的实验表明，其性能显著优于当前最优的扩展卡尔曼滤波（EKF）方法，在精度和鲁棒性方面均有明显提升。
原文摘要 · Abstract (English)
Range-only Simultaneous Localisation and Mapping (RO-SLAM) is of interest due to its practical applications in ultra-wideband (UWB) and Bluetooth Low Energy (BLE) localisation in terrestrial and aerial applications and acoustic beacon localisation in submarine applications. In this work, we consider a mobile robot equipped with an inertial measurement unit (IMU) and a range sensor that measures distances to a collection of fixed landmarks. We derive an equivariant filter (EqF) for the RO-SLAM problem based on a symmetry Lie group that is compatible with the range measurements. The proposed filter does not require bootstrapping or initialisation of landmark positions, and demonstrates robustness to the no-prior situation. The filter is demonstrated on a real-world dataset, and it is shown to significantly outperform a state-of-the-art EKF alternative in terms of both accuracy and robustness.
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