Multi-Sensor Map Matching Techniques for Autonomous Land Vehicle Navigation
This thesis proposes a method for tight integration of digital map and multi sensors including Dead Reckoning (DR) system and Precise Point Positioning (PPP). First, the digital map is tightly coupled with the DR system, including stereo Visual Odometer, Light Detection And Ranging (LiDAR) Odometer and reduced Inertial Measurement Unit (IMU), including two horizontal accelerometers and one vertical gyro. The algorithm starts with stereo Visual Odometry to estimate six Degree of Freedom (DoF) ego motion including rotation and translation parameters to register the point clouds from previous epoch to the current epoch. Afterwards, a Generalized Iterative Closest Point (GICP) algorithm is used to refine the stereo Visual Odometry motion estimation. Then, an Extended Kalman Filter (EKF) is used to integrate the forward velocity and azimuth obtained by Visual-LiDAR Odometer and reduced IMU outputs to provide the final navigation solution. This integrated navigation solution is the input to the fuzzy logic based Map Matching (MM) algorithm, which takes the imprecise and noisy inputs and gives the crisp outputs. The fuzzy logic MM goal is to identify the correct road link, and to determine the vehicle location on the selected road link. The proposed fuzzy logic MM consists of two distinct steps: 1) The Initial Map matching Process (IMP) and 2) The Subsequent Map matching Process (SMP). The proposed map matching algorithm improves integrated multi sensors (stereo Visual-LiDAR and reduced IMU) position accuracy by constraining the vehicle location on the road. The map matching provides close-loop controls for the Dead Reckoning (DR) drift errors by feeding back the map matched position and road link azimuth to the reduced IMU mechanization. This research proposes a new software system for tight integration of kinematic PPP and digital map as well. The PPP provides the navigation solution for MM and MM finds the correct road link and improves PPP performance by providing the map matched position and link azimuth as feedbacks to the Kalman Filter (KF) of PPP. In this research two datasets were used. 1) The datasets from KITTI (Karlsruhe Institute of Technology and Toyota technological Institute) to tightly couple digital map and integrated stereo Visual-LiDAR and reduced IMU, 2) The datasets collected by Positioning and Mobile Information System (PMIS) Group at University of Calgary to tightly couple digital map and integrated stereo Visual Odometry (VO) and reduced IMU and to tightly couple kinematic PPP and digital map. The results show that Visual Odometry (VO)-LiDAR is more accurate than Wheel Odometer, because it provides azimuth aiding to vertical gyro, resulting in a more reliable and accurate system. A low-cost system is developed by using two cameras plus reduced IMU. The cost of such a system will be reduced than using full tactical MEMS (Micro-Electro-Mechanical Sensor) based IMUs, because two cameras are cheaper than full tactical MEMS based IMUs. The results indicate that integrated stereo Visual-LiDAR Odometry and reduced IMU can achieve accuracy at the level of the state of the art. Moreover, tight integration of digital map and integrated stereo Visual-LiDAR Odometry and reduced IMU can achieve considerably better accuracy than existing methods. Moreover, tight integration of digital map/DR gives considerably higher correct link identification rate and lower Root Mean Square Error (RMSE) than a loose integration of digital map/DR. In addition, tight integration of digital map and kinematic PPP outperforms stand-alone PPP and reduces the horizontal RMSE and the convergence time of the float ambiguities.
Geodesy, Remote Sensing, Robotics
Balazadegan Sarvrood, Y. (2016). Multi-Sensor Map Matching Techniques for Autonomous Land Vehicle Navigation (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/27039