Simultaneous Odometry, Mapping and Object Tracking with a Compact Automotive Radar

This paper presents the first results of an experimental study about the possibility to implement basic vehicle odometry, road structure mapping and moving object tracking functionalities by combining a compact automotive radar sensor with appropriate digital signal processing algorithms. In the context of the study, radar raw data sets for real-world road scenarios, including static and moving objects, are first collected from a vehicle-mounted, single-chip 77 GHz automotive radar transceiver and stored on a hard disk. The acquired raw data sets are then processed offline by an algorithm which attempts to detect stationary and moving road objects, estimate odometry data for the radar vehicle and for the detected moving objects and build a map of the detected road structures. Considerations on the applicability of the presented approach and possible extensions of the research work are discussed in conclusion.

[1]  Jens Klappstein,et al.  Automotive radar gridmap representations , 2015, 2015 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM).

[2]  Alain Chantre,et al.  Advanced silicon technologies for wireless communications , 2007, 2007 Ph.D Research in Microelectronics and Electronics Conference.

[3]  Nils Appenrodt,et al.  Radar contribution to highly automated driving , 2014, 2014 44th European Microwave Conference.

[4]  Marwan Younis,et al.  Radar 2020: The future of radar systems , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[5]  Hong Chen,et al.  Predictive Cruise Control Using High-Definition Map and Real Vehicle Implementation , 2018, IEEE Transactions on Vehicular Technology.

[6]  Nils Appenrodt,et al.  "Automotive radar the key technology for autonomous driving: From detection and ranging to environmental understanding" , 2016, 2016 IEEE Radar Conference (RadarConf).

[8]  Jinling Wang,et al.  High definition map-based vehicle localization for highly automated driving: Geometric analysis , 2017, 2017 International Conference on Localization and GNSS (ICL-GNSS).

[9]  Wolfgang Menzel,et al.  Antenna Concepts for Millimeter-Wave Automotive Radar Sensors , 2012, Proceedings of the IEEE.

[10]  Jungyu Kang,et al.  Robust Ego-motion Estimation and Map Matching Technique for Autonomous Vehicle Localization with High Definition Digital Map , 2018, 2018 International Conference on Information and Communication Technology Convergence (ICTC).

[11]  Klaus C. J. Dietmayer,et al.  Radar-interference-based bridge identification for collision avoidance systems , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[12]  Holger H. Meinel,et al.  Evolving automotive radar — From the very beginnings into the future , 2014, The 8th European Conference on Antennas and Propagation (EuCAP 2014).

[13]  John J. Leonard,et al.  Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age , 2016, IEEE Transactions on Robotics.

[14]  Martin David Adams,et al.  Evidential versus Bayesian Estimation for Radar Map Building , 2006, 2006 9th International Conference on Control, Automation, Robotics and Vision.

[15]  G. Maiellaro,et al.  A 24-GHz transceiver with RF power envelope digital control for automotive radar ICs , 2017, 2017 12th European Microwave Integrated Circuits Conference (EuMIC).

[16]  Klaus C. J. Dietmayer,et al.  A fast probabilistic ego-motion estimation framework for radar , 2015, 2015 European Conference on Mobile Robots (ECMR).

[17]  Hao Wang,et al.  Robust and Precise Vehicle Localization Based on Multi-Sensor Fusion in Diverse City Scenes , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[18]  T. O. Jones,et al.  Automotive radar: A brief review , 1974 .