论文标题
Fisher Information Field：一张高效且可区分的知觉计划图
Fisher Information Field: an Efficient and Differentiable Map for Perception-aware Planning
论文作者
论文摘要
考虑到计划时间内的视觉定位精度可以优先考虑机器人运动，该机器人运动可以更好地本地化，因此具有改善基于视觉的导航的潜力，尤其是在视觉降低的环境中。为了将有关本地化准确性的知识纳入运动计划算法中，一个核心任务是量化以6个自由度姿势为定位带来的图像所拍摄的图像的信息，这通常由Fisher Information表示。但是，从一组稀疏地标（即点云）中计算出渔民信息，这是视觉定位的最常见地图，这是效率低下的。这种方法随环境中地标的数量线性扩展，不允许重新使用计算的Fisher信息。为了克服这些弊端，我们提出了第一个专用地图表示，以评估6个自由度视觉视觉定位的费舍尔信息，以进行感知感知运动计划。通过仔细制定Fisher信息和传感器的可见性，我们可以将旋转不变组件与Fisher信息分开，并将其存储在Voxel网格中，即Fisher Information Field。对于已知环境，只需要一次执行此步骤。然后可以在恒定时间内从场地上计算出的渔民信息，从而消除了在计划时间在所有3D地标进行昂贵的迭代。实验结果表明，所提出的Fisher信息字段可以应用于不同的运动计划算法，并且比直接使用点云更快。此外，提出的MAP表示是可区分的，在轨迹优化算法中使用时，与点云相比，性能更好。
Considering visual localization accuracy at the planning time gives preference to robot motion that can be better localized and thus has the potential of improving vision-based navigation, especially in visually degraded environments. To integrate the knowledge about localization accuracy in motion planning algorithms, a central task is to quantify the amount of information that an image taken at a 6 degree-of-freedom pose brings for localization, which is often represented by the Fisher information. However, computing the Fisher information from a set of sparse landmarks (i.e., a point cloud), which is the most common map for visual localization, is inefficient. This approach scales linearly with the number of landmarks in the environment and does not allow the reuse of the computed Fisher information. To overcome these drawbacks, we propose the first dedicated map representation for evaluating the Fisher information of 6 degree-of-freedom visual localization for perception-aware motion planning. By formulating the Fisher information and sensor visibility carefully, we are able to separate the rotational invariant component from the Fisher information and store it in a voxel grid, namely the Fisher information field. This step only needs to be performed once for a known environment. The Fisher information for arbitrary poses can then be computed from the field in constant time, eliminating the need of costly iterating all the 3D landmarks at the planning time. Experimental results show that the proposed Fisher information field can be applied to different motion planning algorithms and is at least one order-of-magnitude faster than using the point cloud directly. Moreover,the proposed map representation is differentiable, resulting in better performance than the point cloud when used in trajectory optimization algorithms.
