A probabilistic framework for object search with 6-DOF pose estimation

This article presents a systematic approach to the problem of autonomous 3D object search in indoor environments, using a two-wheeled non-holonomic robot equipped with an actuated stereo-camera head and processing done on a single laptop. A probabilistic grid-based map encodes the likelihood of object existence in each cell and is updated after each sensing action. The updating schema incorporates characteristic parameters modeled after the robot's sensing modalities and allows for sequential updating via Bayesian recursion methods. Two types of sensing modalities are used to update the map: a coarse search method (global search) based on a color histogram approach, and a more refined search method (local search) based on Scale-Invariant Feature Transform (SIFT) feature matching. If the local search correctly locates the desired object, its 6-DOF pose is estimated using stereo applied to each SIFT feature (i.e. 3D SIFT feature), which is then fed as measurements into an Extended Kalman Filter (EKF) for sustained tracking. If the local search fails to locate the desired object in a particular cell, the cell is updated in the probability map and the next peak probability cell is identified and planned to using a separate grid-based costmap populated via obstacle detection from stereo, with planning done using an A* planner. Experimental results obtained from the use of this method on a mobile robot are presented to illustrate and validate the approach, confirming that the search strategy can be carried out with modest computation on a single laptop.