Segmentation and Factorization-Based Motion and Structure Estimation for Long Image Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
MLESAC: a new robust estimator with application to estimating image geometry
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Multiple view geometry in computer vision
Multiple view geometry in computer vision
Presence: Teleoperators and Virtual Environments
Hybrid Inertial and Vision Tracking for Augmented Reality Registration
VR '99 Proceedings of the IEEE Virtual Reality
Real-Time Simultaneous Localisation and Mapping with a Single Camera
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
An Invitation to 3-D Vision: From Images to Geometric Models
An Invitation to 3-D Vision: From Images to Geometric Models
An Efficient Solution to the Five-Point Relative Pose Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Epipolar Constraints for Vision-Aided Inertial Navigation
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Fusing Visual and Inertial Sensing to Recover Robot Ego-motion
Journal of Robotic Systems
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
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As autonomous vehicle systems become more prevalent, their navigation capabilities become increasingly critical. Currently most systems rely on a combined GPS/INS solution for vehicle pose computation, while some systems use a video-based approach. One problem with a GPS/INS approach is the possible loss of GPS data, especially in urban environments. Using only INS in this case causes significant drift in the computed pose. The video-based approach is not always reliable due to its heavy dependence on image texture. Our approach to autonomous vehicle navigation exploits the best of both of these by coupling an outlier-robust video-based solution with INS when GPS is unavailable. This allows accurate computation of the system's current pose in these situations. In this paper we describe our system design and provide an analysis of its performance, using simulated data with a range of different noise levels.