To enhance accuracy of robot state estimation, perception-aware (or active sensing) methods seek trajectories that minimize uncertainty. To this aim, one possibility is to seek trajectories that minimize the final covariance of an extended Kalman filter (EKF), w.r.t. its control inputs over a given horizon. However, this can be computationally demanding. In this article, we derive novel backpropagation analytical formulas for the derivatives of the final covariance of an EKF w.r.t. its inputs. We then leverage the obtained analytical gradients as an enabling technology to derive perception-aware optimal motion plans. Simulations validate the approach, showcasing improvements in both estimation accuracy and execution time. Experimental results on a real large ground vehicle also support the method.

翻译：为提高机器人状态估计精度，感知感知（或主动感知）方法寻求能够最小化不确定性的轨迹。为此，一种可能的方案是在给定预测范围内，寻求相对于扩展卡尔曼滤波（EKF）控制输入最小化其最终协方差的轨迹。然而，这通常计算代价高昂。本文推导了EKF最终协方差相对于其输入导数的全新反向传播解析公式。进而利用所获得的解析梯度作为使能技术，推导出感知感知最优运动规划方案。仿真结果验证了该方法在估计精度和执行时间方面的提升效果。真实大型地面车辆的实验结果也支持了该方法的有效性。