发表机构
Lehigh University（理海大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出TRACE协议，通过仅共享射线透射率和辐射度聚合值，在保护各机器人私有3D高斯泼溅地图的同时，实现高精度次优视角选择，接近集中式性能。
AI 中文摘要
分享光线，而非地图。我们研究了一组机器人团队的次优视角选择问题，每个机器人构建自己的3D高斯泼溅地图并保持其私密性。一个机器人沿其自身路径选择关于泼溅的期望信息增益（EIG）最大的视角。该增益依赖于其他地图。其他机器人的泼溅会遮挡自身泼溅并在其后方发光，因此增益必须针对合并后的地图进行评估。没有机器人拥有该合并地图。我们证明，这种耦合仅通过两个射线量传递，即泼溅前方的透射率和其后的辐射度，且两者都是射线命中次数的总和。因此，它们可跨机器人分解，每个机器人沿候选视角的射线，在其自身地图的深度区间内对它们求和，并将这些和及其姿态导数发送出去。规划视角的机器人将其转换为在SO(3)上的EIG和梯度。为EIG而通信的透射率和辐射度聚合值，赋予了该协议其名称：TRACE。没有机器人共享其泼溅，且消息大小不随地图增长。我们证明，除非泼溅后方的深度区间混合了两个机器人的命中，否则重建是精确的，否则我们给出误差界限。在Habitat-Sim中进行的超过100次次优视角决策中，TRACE在83.3%的情况下选择的航向与集中式方法相差在15度以内，其视角达到集中式EIG的97.9%。
英文摘要
Share the light, not the map. We study next-best-view selection for a team of robots, each of which builds its own 3D Gaussian Splatting map and keeps it private. A robot picks the view with the largest expected information gain (EIG) about the splats along its own path. This gain depends on the other maps. Their splats occlude its own and shine behind them, so the gain has to be evaluated against the pooled map. No robot has this map. We show that the coupling passes through only two ray quantities, the transmittance in front of a splat and the radiance behind it, and that both are sums over the hits of the ray. Hence, they decompose across the robots, and each robot sums them over depth bins in its own map, along the rays of a candidate view, and sends the sums with their pose derivatives. The robot planning the view turns them into its EIG and gradient on SO(3). Transmittance and Radiance Aggregates, communicated for the EIG, give the protocol its name: TRACE. No robot shares its splats, and the message size does not grow with a map. We prove that the reconstruction is exact unless a depth bin behind a splat mixes hits of two robots, and we bound the error otherwise. Over 100 next-best-view decisions in Habitat-Sim, TRACE picks a heading within 15 degrees of the centralized one in 83.3% of the cases, and its views reach 97.9% of the centralized EIG.
