Laser Scanner and Camera Fusion for Automatic Obstacle Classification in ADAS Application

Reliability and accuracy are key in state of the art Driving Assistance Systems and Autonomous Driving applications. These applications make use of sensor fusion for trustable obstacle detection and classification in any meteorological and illumination condition. Laser scanner and camera are widely used as sensors to fuse because of its complementary capabilities. This paper presents some novel techniques for automatic and unattended data alignment between sensors, and Artificial Intelligence techniques are used to use laser point clouds not only for obstacle detection but also for classification.. Information fusion with classification information from both laser scanner and camera improves overall system reliability.

[1]  Cristiano Premebida,et al.  Performance of laser and radar ranging devices in adverse environmental conditions , 2009 .

[2]  Zezhi Chen,et al.  Road vehicle classification using Support Vector Machines , 2009, 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[3]  Cristiano Premebida,et al.  A cascade classifier applied in pedestrian detection using laser and image-based features , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[4]  José Eugenio Naranjo,et al.  Environment perception based on LIDAR sensors for real road applications , 2011, Robotica.

[5]  Jesús García Herrero,et al.  Context aided pedestrian detection for danger estimation based on laser scanner and computer vision , 2014, Expert Syst. Appl..

[6]  Takeo Kanade,et al.  Extrinsic calibration of a single line scanning lidar and a camera , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[7]  Luca Mazzei,et al.  Automated extrinsic laser and camera inter-calibration using triangular targets , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[8]  Aurelio Ponz,et al.  IVVI 2.0: An intelligent vehicle based on computational perception , 2014, Expert Syst. Appl..

[9]  Yunhui Liu,et al.  An algorithm for extrinsic parameters calibration of a camera and a laser range finder using line features , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  Cindy Cappelle,et al.  3D triangulation based extrinsic calibration between a stereo vision system and a LIDAR , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[11]  Roland Siegwart,et al.  Human detection using multimodal and multidimensional features , 2008, 2008 IEEE International Conference on Robotics and Automation.

[12]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[13]  Cristiano Premebida,et al.  LIDAR and vision‐based pedestrian detection system , 2009, J. Field Robotics.

[14]  Vincent Frémont,et al.  Extrinsic calibration between a multi-layer lidar and a camera , 2008, 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems.

[15]  Aurelio Ponz,et al.  Automatic laser and camera extrinsic calibration for data fusion using road plane , 2014, 17th International Conference on Information Fusion (FUSION).

[16]  Sergiu Nedevschi,et al.  Automatic one step extrinsic calibration of a multi layer laser scanner relative to a stereo camera , 2010, Proceedings of the 2010 IEEE 6th International Conference on Intelligent Computer Communication and Processing.