GPU加速的自主探索路径相关边际信息增益计算
GPU-Accelerated Path-Dependent Marginal Information Gain for Autonomous Exploration
- Institute for Systems and Robotics（系统与机器人研究所）
- Instituto Superior Técnico（里斯本高等技术学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出GPU加速的路径相关边际信息增益计算方法，通过深度缓冲区识别观测重叠，在保持精确性的同时大幅提速，并集成于探索规划器，显著减少覆盖率时间。
AI中文摘要：
自主探索要求机器人基于预期信息增益和执行成本持续评估候选视点。基于采样的规划器通过体积光线投射估算增益，但由于计算成本高昂，在评估候选时假设相互独立，忽略了同一路径上视点之间的重叠。本研究提出了一种GPU加速的路径相关边际信息增益计算方法，不再存储和合并每条候选路径上已观测的未知体素，而是使用深度缓冲区表示先前的观测。候选光线被投影到其祖先的深度缓冲区中，以识别观测重叠并排除预期已被观测的区域。规划树按深度顺序评估，以维持视点与其优化偏航角之间的依赖关系，同时各层的候选节点和光线在GPU上并行处理。所提方法保持在基于体素哈希图计算的精确边际增益的5-10%范围内，在桌面GPU上加速高达118倍，在NVIDIA Jetson Orin NX上加速28倍。该方法已集成到两个基于采样的探索规划器中，并在三个仿真环境中进行了评估，在六个评估的规划器-环境组合中，边际增益在五个组合中减少了达到95%覆盖率的时间。真实世界实验也显示达到95%覆盖率的时间减少了30%，且探索终止时间更早。
英文摘要：
Autonomous exploration demands that robots continuously evaluate candidate viewpoints based on their expected information gain and execution cost. Sampling-based planners estimate this gain by volumetric raycasting and, due to its computational cost, evaluate candidates under an assumption of mutual independence, ignoring the overlap between viewpoints along the same path. This work presents a GPU-accelerated method for computing path-dependent marginal information gain, where instead of storing and merging the observed unknown voxels along each candidate path, previous observations are represented using depth buffers. Candidate rays are projected into the depth buffers of their ancestors to identify observation overlap and exclude regions expected to be observed. The planning tree is evaluated in depth order to maintain the dependency between viewpoints and their optimized yaws, while candidate nodes and rays at each level are processed in parallel on the GPU. The proposed method stays within 5-10% of the exact marginal gain computed using voxel hash maps, with speed-ups of up to 118x on a desktop GPU and 28x on an NVIDIA Jetson Orin NX. The method was integrated into two sampling-based exploration planners and evaluated in three simulation environments, where marginal gain reduced the time to 95% coverage in five of the six evaluated planner-environment combinations. Real-world experiments also showed a 30% reduction in the time to 95% coverage, as well as earlier exploration termination times.
