Wheel Odometry Aided Visual-Inertial Odometry in Winter Urban Environments
Date
2021-01-20
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Abstract
Over the last decade or so, the world has witnessed the rapid changes in the way people drive. How to ensure the navigation performance in challenging environments such as complex urban canyon environments or winter road environment with a relatively low-cost navigation system has become a popular research topic. Global Navigation Satellite System (GNSS) positioning is commonly used for land vehicle navigation. However, the accuracy of GNSS positioning is reduced in such challenging environments due to obstructions and multipath effects. Thus, the development of an alternative, accurate, inexpensive, and self-contained land vehicle navigation systems to bridge the GNSS gaps is significant for land vehicle navigation systems. Visual-inertial odometry (VIO) is an accurate, inexpensive, and complementary approach for land vehicle navigation in GNSS signal-denied environments. VIO is subject to scale drift because it estimates forward direction translation using distant feature points that are generally located only in the forward direction. Wheel odometer measurements can be obtained from the CANBUS interface of most modern passenger vehicles and these provide reliable estimates of the forward wheel speed. In this thesis, an innovative approach to incorporate wheel odometry (WO) and non-holonomic constraints (NHC) together with tightly-coupled monocular visual-inertial odometry using the Multi-State Constraint Kalman Filter (MSCKF) is proposed and implemented. The algorithm is first validated using the public KITTI Dataset [1] with simulated wheel odometer data. Then, the KAIST Complex Urban Dataset [2] is used to test the performance of IMU+Vision+WO integration system in urban canyon environments. Winter driving data is collected in Calgary and used to evaluate the influence of winter road conditions on the proposed algorithm. The results demonstrate that WO and NHC are able to control the scale drift, and as a result are able to control both scale and orientation over longer periods than IMU+Vision alone. IMU+Vision+WO achieved 1.814 m horizontal position error in a 1-minute drive in an urban canyon environment in the KAIST Complex Urban Dataset and 19.649 m and 3.456 m horizontal position errors in two 1-minute drives in our Calgary winter urban environment. The results demonstrate that IMU+Vision+WO is a very promising method to bridge the GNSS outages and performs very well in some challenging environments.
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Keywords
VIO, MSCKF, IMU, Wheel Odometer, Winter, Urban, Land Vehicle Navigation
Citation
Huang, C. (2021). Wheel Odometry Aided Visual-Inertial Odometry in Winter Urban Environments (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.