Real-time dense occupancy mapping using spatial correlation of point clouds

Abstract

Understanding a changing environment is one of the essential elements in autonomous robots and systems. A depth sensor or a LiDAR provides the geometric information of the surroundings as a point cloud. As a fundamental technique for robotics applications such as motion planning and collision avoidance, occupancy mapping techniques from sensor data have been studied for decades. However, updating robust representation with real-time processing speed remains a challenging problem of occupancy mapping in a dynamic environment, since the point cloud data of a single scan contains partial geometry observations of the environment. This dissertation studies the occupancy mapping techniques that update their dense occupancy representations in real-time, exploiting spatial correlation of point cloud data. Specifically, we propose a method for real-time occupancy updates in a dynamic environment

1) an acceleration algorithm exploiting the geometric update patterns of an occupancy map. Furthermore, we propose two approaches to update the dense occupancy representation of the environment given sparse sensor data

2) regression method using correlation among occupancy observations, and 3) deep-learning network embedding prior knowledge about the spatial correlation of measuring sensor data.