用于主动感知的基于反向传播的EKF协方差解析导数
Backpropagation-Based Analytical Derivatives of EKF Covariance for Active Sensing
AI总结：
针对主动感知中EKF协方差最小化轨迹求解计算量大的问题，提出基于反向传播的EKF协方差解析导数计算方法，用于感知感知型运动规划，在仿真和实车实验中均验证了有效性且执行效率优于PyTorch自动微分。
摘要：
为提升机器人状态估计的精度，主动感知（或称感知感知型）方法致力于寻找能最大化传感器采集信息量的轨迹。为实现这一目标，一种可行思路是寻找在给定时域内相对于控制输入，能最小化扩展卡尔曼滤波器（EKF）输出的（估计误差）协方差矩阵的轨迹，但该方法计算量极大。本文推导了全新的反向传播解析公式，用于计算EKF协方差矩阵相对于其所有输入的导数。随后，我们将所得解析梯度作为支撑技术，推导感知感知型最优运动规划。仿真实验验证了该方法的有效性，结果显示其执行时间相比PyTorch的自动微分有显著提升，实车实验结果也支撑了该方法的可行性。
英文摘要：
To enhance accuracy of robot state estimation, active sensing (or perception-aware) methods seek trajectories that maximize the information gathered by the sensors. To this aim, one possibility is to seek trajectories that minimize the (estimation error) covariance matrix output by an extended Kalman filter (EKF), w.r.t. its control inputs over a given horizon. However, this is computationally demanding. In this article, we derive novel backpropagation analytical formulas for the derivatives of the covariance matrices of an EKF w.r.t. all 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 execution time, notably over PyTorch's automatic differentiation. Experimental results on a real vehicle also support the method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
