Optical Flow-Based Odometry for Underground Tunnel Exploration

As military operations in degraded or GPS-denied environments continue to increase in frequency and importance, there is an increased necessity to be able to determine precision location within these environments. Furthermore, authorities are finding a record number of tunnels along the U.S.-Mexico border; therefore, underground tunnel characterization is becoming a high priority for U.S. Homeland Security as well. This thesis investigates the performance of a new image registration technique based on a two camera optical- flow configuration using phase correlation techniques. These techniques differ from other image based navigation methods but present a viable alternative increasing autonomy and answering the tunnel based navigation problem. This research presents an optical flow based image registration technique and validates its use with experimental results on a mobile vehicle. Results reveal that further extension to pose estimation yields accuracy within 1.3 cm

Fusion of Imaging and Inertial Sensors for Navigation

The motivation of this research is to address the limitations of satellite-based navigation by fusing imaging and inertial systems. The research begins by rigorously describing the imaging and navigation problem and developing practical models of the sensors, then presenting a transformation technique to detect features within an image. Given a set of features, a statistical feature projection technique is developed which utilizes inertial measurements to predict vectors in the feature space between images. This coupling of the imaging and inertial sensors at a deep level is then used to aid the statistical feature matching function. The feature matches and inertial measurements are then used to estimate the navigation trajectory using an extended Kalman filter. After accomplishing a proper calibration, the image-aided inertial navigation algorithm is then tested using a combination of simulation and ground tests using both tactical and consumer- grade inertial sensors. While limitations of the Kalman filter are identified, the experimental results demonstrate a navigation performance improvement of at least two orders of magnitude over the respective inertial-only solutions

An empirical comparison of methods for image-based motion estimation

This paper presents a comparison between methods that estimate motion of a camera from a sequence of video images. We implemented two methods: a homography based method that assumes planar environments; and shape-from-motion, a general method that can deal with a fully three dimensional world. Both methods were formulated in an iterative, online form to produce estimates of camera motion. We discuss a trade-off in accuracy and run time efficiency based on experimental results for these two general methods in relation to ground truth. We show how a variation of the homography method can produce accurate results in some cases when the environment is non-planar with low computational cost.</p