In this paper, we study how to build a vision-based system for global localization with accuracies within 10 cm. for robots and humans operating both indoors and outdoors over wide areas covering many square kilometers.
Matching Vehicles Under Large Pose Transformations Using Approximate 3D Models and Piecewise MRF Model
We propose a robust object recognition method based on approximate 3D models that can effectively match objects under large viewpoint changes and partial occlusion.
Visual Odometry System Using Multiple Stereo Cameras and Inertial Measurement Unit
In this paper, we present a robust method that addresses these challenges using a human wearable system with two pairs of backward and forward looking stereo cameras together with an inertial measurement unit (IMU).
On-The-Move Independently Moving Target Detection
This paper describes a system for automatically detecting potential targets (that pop-up or move into view) and to cue the operator to potential threats. We present a 3D approach for detecting and tracking such independently moving targets with multiple monocular cameras.
Precise Visual Navigation Using Multi-Stereo Vision and Landmark Matching
In this paper, we propose a set of techniques which greatly reduce the long term drift and also improve its robustness to many failure conditions.
Vehicle Fingerprinting for Reacquisition and Tracking in Videos
We address the problem of vehicle matching when multiple observations of a vehicle are separated in time such that frames of observations are not contiguous, thus prohibiting the use of standard frame-to-frame data association.
Unsupervised Learning of Discriminative Edge Measures for Vehicle Matching Between Non-Overlapping Cameras
This paper proposes a method for matching road vehicles between two non-overlapping cameras. The matching problem is formulated as a same-different classification problem: probability of two observations from two distinct cameras being from the same vehicle or from different vehicles.