Multiple view geometry in computer vision
Multiple view geometry in computer vision
Modelling and Control of Robot Manipulators
Modelling and Control of Robot Manipulators
Spectral Partitioning for Structure from Motion
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Bearings-only localization and mapping
Bearings-only localization and mapping
Real Time Localization and 3D Reconstruction
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A relative map approach to SLAM based on shift and rotation invariants
Robotics and Autonomous Systems
MonoSLAM: Real-Time Single Camera SLAM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Omnidirectional Vision Based Topological Navigation
International Journal of Computer Vision
Long range stereo data-fusion from moving platforms
Long range stereo data-fusion from moving platforms
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
International Journal of Robotics Research
Probabilistic topological maps
Probabilistic topological maps
Improving the Agility of Keyframe-Based SLAM
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
The New College Vision and Laser Data Set
International Journal of Robotics Research
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Incremental smoothing and mapping
Incremental smoothing and mapping
Navigating, Recognizing and Describing Urban Spaces With Vision and Lasers
International Journal of Robotics Research
A relative frame representation for fixed-time bundle adjustment in SFM
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Machine learning for high-speed corner detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Bayesian inference in the space of topological maps
IEEE Transactions on Robotics
FrameSLAM: From Bundle Adjustment to Real-Time Visual Mapping
IEEE Transactions on Robotics
iSAM: Incremental Smoothing and Mapping
IEEE Transactions on Robotics
Efficient Homography-Based Tracking and 3-D Reconstruction for Single-Viewpoint Sensors
IEEE Transactions on Robotics
CAT-SLAM: probabilistic localisation and mapping using a continuous appearance-based trajectory
International Journal of Robotics Research
Three-dimensional SLAM for mapping planetary work site environments
Journal of Field Robotics
Field trial results of planetary rover visual motion estimation in Mars analogue terrain
Journal of Field Robotics
Field testing of visual odometry aided by a sun sensor and inclinometer
Journal of Field Robotics
OpenRatSLAM: an open source brain-based SLAM system
Autonomous Robots
Experience-based navigation for long-term localisation
International Journal of Robotics Research
Robust loop closing over time for pose graph SLAM
International Journal of Robotics Research
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In this paper we describe a relative approach to simultaneous localization and mapping, based on the insight that a continuous relative representation can make the problem tractable at large scales. First, it is well known that bundle adjustment is the optimal non-linear least-squares formulation for this problem, in that its maximum-likelihood form matches the definition of the Cramerâ聙聰Rao lower bound. Unfortunately, computing the maximum-likelihood solution is often prohibitively expensive: this is especially true during loop closures, which often necessitate adjusting all parameters in a loop. In this paper we note that it is precisely the choice of a single privileged coordinate frame that makes bundle adjustment costly, and that this expense can be avoided by adopting a completely relative approach. We derive a new relative bundle adjustment which, instead of optimizing in a single Euclidean space, works in a metric space defined by a manifold. Using an adaptive optimization strategy, we show experimentally that it is possible to solve for the full maximum-likelihood solution incrementally in constant time, even at loop closure. Our approach is, by definition, everywhere locally Euclidean, and we show that the local Euclidean estimate matches that of traditional bundle adjustment. Our system operates online in realtime using stereo data, with fast appearance-based loop closure detection. We show results on over 850,000 images that indicate the accuracy and scalability of the approach, and process over 330 GB of image data into a relative map covering 142 km of Southern England. To demonstrate a baseline sufficiency for navigation, we show that it is possible to find shortest paths in the relative maps we build, in terms of both time and distance. Query images from the web of popular landmarks around London, such as the London Eye or Trafalgar Square, are matched to the relative map to provide route planning goals.