We present a unified probabilistic framework for simultaneous trajectory estimation and planning. Estimation and planning problems are usually considered separately, however, within our framework we show that solving them simultaneously can be more accurate and efficient. The key idea is to compute the full continuous-time trajectory from start to goal at each time-step. While the robot traverses the trajectory, the history portion of the trajectory signifies the solution to the estimation problem, and the future portion of the trajectory signifies a solution to the planning problem. Building on recent probabilistic inference approaches to continuous-time localization and mapping and continuous-time motion planning, we solve the joint problem by iteratively recomputing the maximum a posteriori trajectory conditioned on all available sensor data and cost information. Our approach can contend with high-degree-of-freedom trajectory spaces, uncertainty due to limited sensing capabilities, model inaccuracy, the stochastic effect of executing actions, and can find a solution in real-time. We evaluate our framework empirically in both simulation and on a mobile manipulator.

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probabilistic inference approaches sensor data stochastic effect em joint problem full continuoustime trajectory unified probabilistic framework mobile manipulator limited sensing capabilities model highdegreeoffreedom trajectory spaces uncertainty future actions history approach simultaneous trajectory estimation and planning estimation and planning problems

Frank Dellaert,Jing Dong,Mustafa Mukadam,Byron Boots,.STEAP: simultaneous trajectory estimation and planning. 43 (2),415–434.

Frank Dellaert, et al."STEAP: simultaneous trajectory estimation and planning" 43

Frank Dellaert,Jing Dong,Mustafa Mukadam,Byron Boots,"STEAP: simultaneous trajectory estimation and planning"