A Statistical Update of Grid Representations from Range Sensors
This work addresses incremental improvements in 3D mapping for robotics applications.
The researchers tackled the problem of 3D environment reconstruction from range sensors by developing a statistical model using occupancy grids to reduce discretization errors, and they validated it qualitatively on the KITTI dataset.
In a wide range of robotic applications, being able to create a 3D model of the surrounding environment is a key feature for autonomous tasks. In this research report, we present a statistical model to perform 3D reconstructions of the environment from range sensors using an occupancy grid. To do so, we take into account all the available information obtained from the sensor, considering the distances traversed by the rays in each cell and seeking to reduce reconstruction errors caused by discretization. The approach has been validated qualitatively using the KITTI dataset.