Radar-on-Lidar: metric radar localization on prior lidar maps

Radar and lidar, provided by two different range sensors, each has pros and cons of various perception tasks on mobile robots or autonomous driving. In this paper, a Monte Carlo system is used to localize the robot with a rotating radar sensor on 2D lidar maps. We first train a conditional generative adversarial network to transfer raw radar data to lidar data, and achieve reliable radar points from generator. Then an efficient radar odometry is included in the Monte Carlo system. Combining the initial guess from odometry, a measurement model is proposed to match the radar data and prior lidar maps for final 2D positioning. We demonstrate the effectiveness of the proposed localization framework on the public multisession dataset. The experimental results show that our system can achieve high accuracy for long-term localization in outdoor scenes.

[1]  Paul Newman,et al.  The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[2]  Paul Newman,et al.  Probably Unknown: Deep Inverse Sensor Modelling Radar , 2018, 2019 International Conference on Robotics and Automation (ICRA).

[3]  Yeong Sang Park,et al.  Radar Localization and Mapping for Indoor Disaster Environments via Multi-modal Registration to Prior LiDAR Map , 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[4]  Ingmar Posner,et al.  Masking by Moving: Learning Distraction-Free Radar Odometry from Pose Information , 2019, CoRL.

[5]  Ayoung Kim,et al.  Scan Context: Egocentric Spatial Descriptor for Place Recognition Within 3D Point Cloud Map , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[6]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Paul Newman,et al.  What Could Go Wrong? Introspective Radar Odometry in Challenging Environments , 2019, 2019 IEEE Intelligent Transportation Systems Conference (ITSC).

[8]  Christoph Gustav Keller,et al.  Robust localization based on radar signal clustering , 2016, 2016 IEEE Intelligent Vehicles Symposium (IV).

[9]  Paul Newman,et al.  Precise Ego-Motion Estimation with Millimeter-Wave Radar Under Diverse and Challenging Conditions , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[10]  Roland Siegwart,et al.  Lighting‐invariant Adaptive Route Following Using Iterative Closest Point Matching , 2015, J. Field Robotics.

[11]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[12]  Jinyong Jeong,et al.  MulRan: Multimodal Range Dataset for Urban Place Recognition , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[13]  Yue Wang,et al.  3D LiDAR Map Compression for Efficient Localization on Resource Constrained Vehicles , 2020, IEEE Transactions on Intelligent Transportation Systems.

[14]  Paul Newman,et al.  Radar-only ego-motion estimation in difficult settings via graph matching , 2019, 2019 International Conference on Robotics and Automation (ICRA).

[15]  Bing Wang,et al.  milliMap: Robust Indoor Mapping with Low-cost mmWave Radar , 2019 .

[16]  Paul Newman,et al.  Fast Radar Motion Estimation with a Learnt Focus of Attention using Weak Supervision , 2019, 2019 International Conference on Robotics and Automation (ICRA).

[17]  Roland Chapuis,et al.  Localization and Mapping Using Only a Rotating FMCW Radar Sensor , 2013, Sensors.

[18]  Yue Wang,et al.  3D LiDAR-Based Global Localization Using Siamese Neural Network , 2020, IEEE Transactions on Intelligent Transportation Systems.

[19]  Paul Newman,et al.  1 year, 1000 km: The Oxford RobotCar dataset , 2017, Int. J. Robotics Res..

[20]  Juan D. Tardós,et al.  ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras , 2016, IEEE Transactions on Robotics.

[21]  Jinyong Jeong,et al.  Radar Dataset for Robust Localization and Mapping in Urban Environment , 2019 .

[22]  Yue Wang,et al.  Persistent Stereo Visual Localization on Cross-Modal Invariant Map , 2020, IEEE Transactions on Intelligent Transportation Systems.

[23]  Christoph Gustav Keller,et al.  Landmark based radar SLAM using graph optimization , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).