Evaluation of Occupancy Grid Resolution through a Novel Approach for Inverse Sensor Modeling

Abstract Several robotic applications imply motion in complex and dynamic environments. Occupancy Grids model the surrounding environment by a grid composed of a finite number of cells. The probability whether a cell is occupied or empty is computed and updated iteratively based on sensor measurements by considering their uncertainty through probabilistic models. Even if Occupancy Grids have been widely used in the state-of-the-art, the relation between the cell size, the sensor precision and the inverse sensor model is usually neglected. In this paper, we propose a methodology to build the inverse probabilistic model for single-target sensors. The proposed approach is then applied to a LiDAR in order to evaluate the impact of the variation in the sensor precision and the grid resolution on the inverse sensor model. Based on this study, we finally propose a procedure that allows to choose the suitable grid resolution for obtaining a desired maximum occupancy probability in the inverse sensor model.

[1]  D Gabor,et al.  World modeling. , 1972, Science.

[2]  Wolfram Burgard,et al.  Probabilistic Robotics (Intelligent Robotics and Autonomous Agents) , 2005 .

[3]  Clément Gosselin,et al.  Range data merging for probabilistic octree modeling of 3D workspaces , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[4]  Yassine Ruichek,et al.  Building variable resolution occupancy grid map from stereoscopic system — A quadtree based approach , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[5]  François Blais,et al.  A Comparison of Precision and Accuracy in Triangulation Laser Range Scanners , 2006, 2006 Canadian Conference on Electrical and Computer Engineering.

[6]  Ayoub Al-Hamadi,et al.  Stereo-Camera-Based Urban Environment Perception Using Occupancy Grid and Object Tracking , 2012, IEEE Transactions on Intelligent Transportation Systems.

[7]  Taeyoung Lee,et al.  Bayesian occupancy grid mapping via an exact inverse sensor model , 2016, 2016 American Control Conference (ACC).

[8]  Alberto Elfes,et al.  Occupancy grids: a probabilistic framework for robot perception and navigation , 1989 .

[9]  Hans P. Moravec,et al.  High resolution maps from wide angle sonar , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[10]  Christian Laugier,et al.  Real-time power-efficient integration of multi-sensor occupancy grid on many-core , 2015, 2015 IEEE International Workshop on Advanced Robotics and its Social Impacts (ARSO).

[11]  K. Dietmayer,et al.  Robust Driving Path Detection in Urban and Highway Scenarios Using a Laser Scanner and Online Occupancy Grids , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[12]  Erik Einhorn,et al.  Finding the adequate resolution for grid mapping - Cell sizes locally adapting on-the-fly , 2011, 2011 IEEE International Conference on Robotics and Automation.

[13]  A. Aguado,et al.  Incremental map building using an occupancy grid for an autonomous monocular robot , 2002, 7th International Conference on Control, Automation, Robotics and Vision, 2002. ICARCV 2002..

[14]  Darius Burschka,et al.  Efficient occupancy grid computation on the GPU with lidar and radar for road boundary detection , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[15]  Wolfram Burgard,et al.  OctoMap: an efficient probabilistic 3D mapping framework based on octrees , 2013, Autonomous Robots.

[16]  Mathias Perrollaz,et al.  Computing occupancy grids from multiple sensors using linear opinion pools , 2012, 2012 IEEE International Conference on Robotics and Automation.

[17]  Christian Laugier,et al.  Efficient GPU-based Construction of Occupancy Girds Using several Laser Range-finders , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[18]  Kurt Konolige,et al.  Improved Occupancy Grids for Map Building , 1997, Auton. Robots.