通过高阶运动轨迹信息优化稀疏感知路径，提升追踪精度
Homotopic information gain for sparse active target tracking
- 基于同伦类信息增益规划机器人感知路径
- 仅需更少观测即可达到比传统方法更高的轨迹预测精度
- 适合复杂障碍环境中高效目标追踪场景
移动机器人主动目标追踪问题旨在规划传感轨迹以获取目标观测并预测其未来轨迹。借助概率运动模型，可通过探索所有轨迹预测的信念空间来最大化信息增益。然而，对于多模态运动模型，传统信息增益定义模糊。本文提出一种新方法，聚焦最大化目标同伦类（即高阶运动模式）的信息增益。引入同伦信息增益作为测量带来的预期高阶轨迹信息度量，证明其为度量或低阶信息增益的下界，且在环境中的分布密度与障碍物一致。实验证明，基于同伦信息增益规划的感知路径，在真实与模拟行人数据上均能以更少测量实现更高精度的轨迹估计。
原文摘要 · Abstract (English)
The problem of planning sensing trajectories for a mobile robot to collect observations of a target and predict its future trajectory is known as active target tracking. Enabled by probabilistic motion models, one may solve this problem by exploring the belief space of all trajectory predictions given future sensing actions to maximise information gain. However, for multi-modal motion models the notion of information gain is often ill-defined. This paper proposes a planning approach designed around maximising information regarding the target's homotopy class, or high-level motion. We introduce homotopic information gain, a measure of the expected high-level trajectory information given by a measurement. We show that homotopic information gain is a lower bound for metric or low-level information gain, and is as sparsely distributed in the environment as obstacles are. Planning sensing trajectories to maximise homotopic information results in highly accurate trajectory estimates with fewer measurements than a metric information approach, as supported by our empirical evaluation on real and simulated pedestrian data.
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