Registration and segmentation for 3D map building a solution based on stereo vision and inertial sensors

This article presents a technique for registration and segmentation of dense depth maps provided by a stereo vision system. The vision system uses inertial sensors to give a reference for camera pose. The maps are registered using a modified version of the ICP - Iterative Closest Point algorithm to register dense depth maps obtained from a stereo vision system. The proposed technique explores the integration of inertial sensor data for dense map registration. Depth maps obtained by vision systems, are very point of view dependent, providing discrete layers of detected depth-aligned with the camera. In this work we use inertial sensors to recover camera pose, and rectify the maps to a reference ground plane, enabling the segmentation of vertical and horizontal geometric features and map registration. We propose a real-time methodology to segment these dense depth maps, including segmentation of structures, object recognition, robot navigation or any other task that requires a three-dimensional representation of the physical environment. The aim of this work is a fast real-time system, that can be applied to autonomous robotic systems or to automated car driving systems, for modelling the road, identifying obstacles and roadside features in real-time.