Recursive affine structure and motion from image sequences
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Geometric methods and applications: for computer science and engineering
Geometric methods and applications: for computer science and engineering
The Geometry of the Newton Method on Non-Compact Lie Groups
Journal of Global Optimization
Recursive Estimation of Motion, Structure, and Focal Length
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structure from Motion Causally Integrated Over Time
IEEE Transactions on Pattern Analysis and Machine Intelligence
Experimental Comparison of Techniques for Localization and Mapping Using a Bearing-Only Sensor
ISER '00 Experimental Robotics VII
Bundle Adjustment - A Modern Synthesis
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
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 Efficient Solution to the Five-Point Relative Pose Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Search for Real-Time Vision
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
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
MonoSLAM: Real-Time Single Camera SLAM
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Active matching for visual tracking
Robotics and Autonomous Systems
Parallel Tracking and Mapping on a camera phone
ISMAR '09 Proceedings of the 2009 8th IEEE International Symposium on Mixed and Augmented Reality
Journal of Field Robotics - Visual Mapping and Navigation Outdoors
RSLAM: A System for Large-Scale Mapping in Constant-Time Using Stereo
International Journal of Computer Vision
iSAM2: Incremental smoothing and mapping using the Bayes tree
International Journal of Robotics Research
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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
iSAM: Incremental Smoothing and Mapping
IEEE Transactions on Robotics
Hi-index | 0.00 |
While the most accurate solution to off-line structure from motion (SFM) problems is undoubtedly to extract as much correspondence information as possible and perform batch optimisation, sequential methods suitable for live video streams must approximate this to fit within fixed computational bounds. Two quite different approaches to real-time SFM - also called visual SLAM (simultaneous localisation and mapping) - have proven successful, but they sparsify the problem in different ways. Filtering methods marginalise out past poses and summarise the information gained over time with a probability distribution. Keyframe methods retain the optimisation approach of global bundle adjustment, but computationally must select only a small number of past frames to process. In this paper we perform a rigorous analysis of the relative advantages of filtering and sparse bundle adjustment for sequential visual SLAM. In a series of Monte Carlo experiments we investigate the accuracy and cost of visual SLAM. We measure accuracy in terms of entropy reduction as well as root mean square error (RMSE), and analyse the efficiency of bundle adjustment versus filtering using combined cost/accuracy measures. In our analysis, we consider both SLAM using a stereo rig and monocular SLAM as well as various different scenes and motion patterns. For all these scenarios, we conclude that keyframe bundle adjustment outperforms filtering, since it gives the most accuracy per unit of computing time.