UCLA

We experimentally demonstrate that an uncertainty-aware framework for robotic navigation and vision is able to navigate a robotic platform around an obstacle without choosing an overly long and conservative path. The framework contains many interconnected experimental pieces, including monocular visual-inertial odometry (VIO) based on an Extended Kalman Filter (EKF), a recurrent neural network that predicts future covariance estimates from the EKF, a model predictive controller that uses uncertainty in its cost function, and an object detection network. Each interconnected piece is an algorithm that makes assumptions about its inputs, which are the outputs of another piece. The rest of the thesis is a systems validation exercise that examines several of these assumptions for validity and finds that they are largely not true. First, we learn that uncertainty estimates of the commonly used EKF are overconfident, but that the overconfidence is systematic and correctable. Next, we examine the distribution of feature track errors and find that not only are the errors not zero-mean Gaussian, the errors are dependent on motion type, speed, and the type of feature tracking algorithm used. We then quantify the effect of attribution errors, Gaussian noise, and drift on performance and uncertainty estimates of the VIO algorithm used in the framework. Finally, we attempt to characterize the uncertainty of image classification networks in a manner appropriate for online navigation. To our knowledge, the proposed architecture is new and this dissertation is the first time a systems validation exercise has focused on uncertainty estimation.