Simultaneous Scene Reconstruction and Auto-Calibration Using Constrained Iterative Closest Point for 3D Depth Sensor Array

Being able to monitor a large area is essential for intelligent warehouse automation. Complete depth map of a plant floor allows Automated Guided Vehicles (AGV) to navigate the environment and safely interact with nearby people and equipment, eliminating the need for installation of guide tracks and range sensors on individual robots. A single camera does not have sufficient field of view irresolution to monitor a large scene, and a camera mounted on a moving platform introduces delays and blind spots that could put people at risk in busy areas. Multi-camera arrays are needed in order to reconstruct the scene from simultaneous captures. Existing iterative closest point (ICP) based algorithms fail to produce meaningful results due to ICP attempting to minimize Euclidean distance between non-matching pairs. This paper describes a method for accurate and computationally efficient simultaneous scene reconstruction and auto-calibration using depth maps captured with multiple downward looking overhead cameras. The proposed method extends upon standard ICP algorithm by incorporating constraints imposed by the camera setup. The common field of view constraint imposed on the ICP algorithm matches a subset of points that are simultaneously in two camera´s field of view. The planar constraint restricts the search space for closest points between 2point clouds to be on a projected planar surface. To simulate a typical warehouse environment, depth maps captured from two overhead Microsoft Kinect cameras were used to evaluate the effectiveness of the proposed algorithm. The results indicate the proposed algorithm successfully reconstructed the scene and produced auto-calibrated extrinsic camera matrix, where as standard ICP algorithm did not generate meaningful results.