Multiple view geometry in computer vision
Multiple view geometry in computer vision
Recursive Estimation of Motion, Structure, and Focal Length
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Efficient Solution to the Five-Point Relative Pose Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
KALMANSAC: Robust Filtering by Consensus
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Real Time Localization and 3D Reconstruction
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
MonoSLAM: Real-Time Single Camera SLAM
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pose Priors for Simultaneously Solving Alignment and Correspondence
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
A Comparative Analysis of RANSAC Techniques Leading to Adaptive Real-Time Random Sample Consensus
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Parallel Tracking and Mapping for Small AR Workspaces
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
Real-time monocular visual odometry for on-road vehicles with 1-point RANSAC
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Divide and Conquer: EKF SLAM in
IEEE Transactions on Robotics
Large-Scale 6-DOF SLAM With Stereo-in-Hand
IEEE Transactions on Robotics
Large-Scale SLAM Building Conditionally Independent Local Maps: Application to Monocular Vision
IEEE Transactions on Robotics
Inverse Depth Parametrization for Monocular SLAM
IEEE Transactions on Robotics
FrameSLAM: From Bundle Adjustment to Real-Time Visual Mapping
IEEE Transactions on Robotics
RT-SLAM: a generic and real-time visual SLAM implementation
ICVS'11 Proceedings of the 8th international conference on Computer vision systems
Visual SLAM Based on Rigid-Body 3D Landmarks
Journal of Intelligent and Robotic Systems
Impact of Landmark Parametrization on Monocular EKF-SLAM with Points and Lines
International Journal of Computer Vision
A novel loop closure detection method in monocular SLAM
Intelligent Service Robotics
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Recently, classical pairwise Structure From Motion (SfM) techniques have been combined with non-linear global optimization (Bundle Adjustment, BA) over a sliding window to recursively provide camera pose and feature location estimation from long image sequences. Normally called Visual Odometry, these algorithms are nowadays able to estimate with impressive accuracy trajectories of hundreds of meters; either from an image sequence (usually stereo) as the only input, or combining visual and propioceptive information from inertial sensors or wheel odometry. This paper has a double objective. First, we aim to illustrate for the first time how similar accuracy and trajectory length can be achieved by filtering-based visual SLAM methods. Specifically, a camera-centered Extended Kalman Filter is used here to process a monocular sequence as the only input, with 6DOF motion estimated. Features are kept live in the filter while visible as the camera explores forward, and are deleted from the state once they go out of view. This permits an increase in the number of tracked features per frame from tens to around a hundred. While improving the accuracy of the estimation, it makes computationally infeasible the exhaustive Branch and Bound search performed by standard JCBB for match outlier rejection. As a second contribution that overcomes this problem, we present here a RANSAC-like algorithm that exploits the probabilistic prediction of the filter. This use of prior information makes it possible to reduce the size of the minimal data subset to instantiate a hypothesis to the minimum possible of 1 point, greatly increasing the efficiency of the outlier rejection stage. Experimental results from real image sequences covering trajectories of hundreds of meters are presented and compared against RTK GPS ground truth. Estimation errors are about 1% of the trajectory for trajectories up to 650 metres.