Body-relative navigation guidance using uncalibrated cameras

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 89-97) and index.The ability to navigate through the world is an essential capability to humans. In a variety of situations, people do not have the time, the opportunity or the capability to learn the layout of the environment before visiting an area. Examples include soldiers in the field entering an unknown building, firefighters responding to an emergency, or a visually impaired person walking through the city. In absence of external source of localization (such as GPS), the system must rely on internal sensing to provide navigation guidance to the user. In order to address real-world situations, the method must provide spatially extended, temporally consistent navigation guidance, through cluttered and dynamic environments. While recent research has largely focused on metric methods based on calibrated cameras, the work presented in this thesis demonstrates a novel approach to navigation using uncalibrated cameras. During the first visit of the environment, the method builds a topological representation of the user's exploration path, which we refer to as the place graph. The method then provides navigation guidance from any place to any other in the explored environment. On one hand, a localization algorithm determines the location of the user in the graph. On the other hand, a rotation guidance algorithm provides a directional cue towards the next graph node in the user's body frame. Our method makes little assumption about the environment except that it contains descriptive visual features. It requires no intrinsic or extrinsic camera calibration, and relies instead on a method that learns the correlation between user rotation and feature correspondence across cameras. We validate our approach using several ground truth datasets. In addition, we show that our approach is capable of guiding a robot equipped with a local obstacle avoidance capability through real, cluttered environments. Finally, we validate our system with nine untrained users through several kilometers of indoor environments.by Olivier Koch.Ph.D

Optimal Image-Aided Inertial Navigation

The utilization of cameras in integrated navigation systems is among the most recent scientific research and high-tech industry development. The research is motivated by the requirement of calibrating off-the-shelf cameras and the fusion of imaging and inertial sensors in poor GNSS environments. The three major contributions of this dissertation are The development of a structureless camera auto-calibration and system calibration algorithm for a GNSS, IMU and stereo camera system. The auto-calibration bundle adjustment utilizes the scale restraint equation, which is free of object coordinates. The number of parameters to be estimated is significantly reduced in comparison with the ones in a self-calibrating bundle adjustment based on the collinearity equations. Therefore, the proposed method is computationally more efficient. The development of a loosely-coupled visual odometry aided inertial navigation algorithm. The fusion of the two sensors is usually performed using a Kalman filter. The pose changes are pairwise time-correlated, i.e. the measurement noise vector at the current epoch is only correlated with the one from the previous epoch. Time-correlated errors are usually modelled by a shaping filter. The shaping filter developed in this dissertation uses Cholesky factors as coefficients derived from the variance and covariance matrices of the measurement noise vectors. Test results with showed that the proposed algorithm performs better than the existing ones and provides more realistic covariance estimates. The development of a tightly-coupled stereo multi-frame aided inertial navigation algorithm for reducing position and orientation drifts. Usually, the image aiding based on the visual odometry uses the tracked features only from a pair of the consecutive image frames. The proposed method integrates the features tracked from multiple overlapped image frames for reducing the position and orientation drifts. The measurement equation is derived from SLAM measurement equation system where the landmark positions in SLAM are algebraically by time-differencing. However, the derived measurements are time-correlated. Through a sequential de-correlation, the Kalman filter measurement update can be performed sequentially and optimally. The main advantages of the proposed algorithm are the reduction of computational requirements when compared to SLAM and a seamless integration into an existing GNSS aided-IMU system

Ground Robot Navigation using Uncalibrated Cameras

Precise calibration of camera intrinsic and extrinsic parameters, while often useful, is difficult to obtain during field operation and presents scaling issues for multi-robot systems. We demonstrate a vision-based approach to navigation that does not depend on traditional camera calibration, and present an algorithm for guiding a robot through a previously traversed environment using a set of uncalibrated cameras mounted on the robot. On the first excursion through an environment, the system builds a topological representation of the robot's exploration path, encoded as a place graph. On subsequent navigation missions, the method localizes the robot within the graph and provides robust guidance to a specified destination. We combine this method with reactive collision avoidance to obtain a system able to navigate the robot safely and reliably through the environment. We validate our approach with ground-truth experiments and demonstrate the method on a small ground rover navigating through several dynamic environments.Charles Stark Draper Laborator