Uncertainty Quantification of Tightly Integrated LiDAR/IMU Localization Algorithms

Safety risk evaluation is critical in autonomous vehicle applications. This research aims to develop, implement, and validate new safety monitoring methods for navigation in Global Navigation Satellite System (GNSS)-denied environments. The methods quantify uncertainty in sensors and algorithms that exploit the complementary properties of light detection and ranging (LiDAR) and inertial measuring units (IMU). This dissertation describes the following four contributions. First, we focus on sensor augmentation for landmark-based localization. We develop new IMU/LiDAR integration methods that guarantee a bound on the integrity risk, which is the probability that the navigation error exceeds predefined acceptability limits. IMU data improves LiDAR position and orientation (pose) prediction and LiDAR limits the IMU error drift over time. In addition, LiDAR return-light intensity measurements improve landmarks recognition. As compared to using the sensors individually, tightly-coupled IMU/LiDAR not only increases pose estimation accuracy but also reduces the risk of incorrectly associating perceived features with mapped landmarks. Second, we consider algorithm improvements. We derive and analyze a new data association method that provides a tight bound on the risk of incorrect association for LiDAR feature-based localization. The new data association criterion uses projections of the extended Kalman filter's (EKF) innovation vector rather than more conventional innovation vector norms. This method decreases the integrity risk by improving our ability to predict the risk of incorrect association. Third, we depart from landmark-based approaches. We develop a spherical grid-based localization method that leverages quantization theory to bound navigation uncertainty. This method is integrated with an iterative EKF to establish an analytical bound on the vehicle's pose estimation error. Unlike landmark-based localization which requires feature extraction and data association, this method uses the entire LiDAR point cloud and is robust to extraction and association failures. Fourth, to validate these methods, we designed and built two testbeds for indoor and outdoor experiments. The indoor testbed includes a sensor platform installed on a rover moving on a figure-eight track in a controlled lab environment. The repeated figure-eight trajectory provides empirical pose estimation error distributions that can directly be compared with analytical error bounds. The outdoor testbed required another set of navigation sensors for reference truth trajectory generation. Sensors were mounted on a car to validate our algorithms in a realistic automotive driving environment.