A Robust Stereo Camera Localization Method with Prior LiDAR Map Constrains

In complex environments, low-cost and robust localization is a challenging problem. For example, in a GPS-denied environment, LiDAR can provide accurate position information, but the cost is high. In general, visual SLAM based localization methods become unreliable when the sunlight changes greatly. Therefore, inexpensive and reliable methods are required. In this paper, we propose a stereo visual localization method based on the prior LiDAR map. Different from the conventional visual localization system, we design a novel visual optimization model by matching planar information between the LiDAR map and visual image. Bundle adjustment is built by using coplanarity constraints. To solve the optimization problem, we use a graph-based optimization algorithm and a local window optimization method. Finally, we estimate a full six degrees of freedom (DOF) pose without scale drift. To validate the efficiency, the proposed method has been tested on the KITTI dataset. The results show that our method is more robust and accurate than the state-of-art ORB-SLAM2.

[1]  Wolfram Burgard,et al.  Place recognition in 3D scans using a combination of bag of words and point feature based relative pose estimation , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[2]  Ming Liu,et al.  Low-Cost GPS-Aided LiDAR State Estimation and Map Building , 2019, 2019 IEEE International Conference on Imaging Systems and Techniques (IST).

[3]  Jinyong Jeong,et al.  Stereo Camera Localization in 3D LiDAR Maps , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

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

[5]  Xiaqing Ding,et al.  Laser Map Aided Visual Inertial Localization in Changing Environment , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[6]  Roland Siegwart,et al.  Normal estimation for pointcloud using GPU based sparse tensor voting , 2012, 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[7]  Yan Lu,et al.  Monocular localization in urban environments using road markings , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[8]  Ming Liu,et al.  PCR-Pro: 3D Sparse and Different Scale Point Clouds Registration and Robust Estimation of Information Matrix for Pose Graph SLAM , 2018, 2018 IEEE 8th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER).

[9]  Max Q.-H. Meng,et al.  Real-Time Multisensor Data Retrieval for Cloud Robotic Systems , 2015, IEEE Transactions on Automation Science and Engineering.

[10]  Davide Scaramuzza,et al.  SVO: Fast semi-direct monocular visual odometry , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[11]  Rafael Grompone von Gioi,et al.  LSD: A Fast Line Segment Detector with a False Detection Control , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Roland Siegwart,et al.  Scale-only visual homing from an omnidirectional camera , 2012, 2012 IEEE International Conference on Robotics and Automation.

[13]  G. Medioni,et al.  Tensor Voting : Theory and Applications , 2000 .

[14]  Wolfram Burgard,et al.  VR-Goggles for Robots: Real-to-Sim Domain Adaptation for Visual Control , 2018, IEEE Robotics and Automation Letters.

[15]  Ryan M. Eustice,et al.  Visual localization within LIDAR maps for automated urban driving , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[16]  Ming Liu,et al.  Efficient segmentation and plane modeling of point-cloud for structured environment by normal clustering and tensor voting , 2014, 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014).

[17]  Roland Siegwart,et al.  DP-Fusion A generic framework for online multi sensor recognition , 2012 .

[18]  Roland Siegwart,et al.  Towards real-time multi-sensor information retrieval in Cloud Robotic System , 2012, 2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI).

[19]  Paul Newman,et al.  Direct Visual Localisation and Calibration for Road Vehicles in Changing City Environments , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

[20]  Roland Siegwart,et al.  A framework for multi-robot pose graph SLAM , 2016, 2016 IEEE International Conference on Real-time Computing and Robotics (RCAR).

[21]  Max Q.-H. Meng,et al.  A Hierarchical Auction-Based Mechanism for Real-Time Resource Allocation in Cloud Robotic Systems , 2017, IEEE Transactions on Cybernetics.

[22]  Ruisheng Wang,et al.  3D building modeling using images and LiDAR: a review , 2013 .

[23]  Roland Siegwart,et al.  A bearing-only 2D/3D-homing method under a visual servoing framework , 2010, 2010 IEEE International Conference on Robotics and Automation.

[24]  Ryan M. Eustice,et al.  Robust LIDAR localization using multiresolution Gaussian mixture maps for autonomous driving , 2017, Int. J. Robotics Res..

[25]  Andrew J. Davison,et al.  DTAM: Dense tracking and mapping in real-time , 2011, 2011 International Conference on Computer Vision.

[26]  Dana H. Ballard,et al.  Generalizing the Hough transform to detect arbitrary shapes , 1981, Pattern Recognit..

[27]  Denis Wolf,et al.  Road marking detection using LIDAR reflective intensity data and its application to vehicle localization , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[28]  G. Klein,et al.  Parallel Tracking and Mapping for Small AR Workspaces , 2007, 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality.

[29]  Olivier Stasse,et al.  MonoSLAM: Real-Time Single Camera SLAM , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Daniel Cremers,et al.  Direct Sparse Odometry , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Roland Siegwart,et al.  The role of homing in visual topological navigation , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[32]  Ming Liu,et al.  Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems , 2019, IEEE Robotics and Automation Letters.

[33]  Daniel Cremers,et al.  LSD-SLAM: Large-Scale Direct Monocular SLAM , 2014, ECCV.