Sparser Relative Bundle Adjustment (SRBA)
Bundle adjustment is the name given to one solution to visual SLAM based on maximum-likelihood estimation (MLE) over the space of map features and camera poses. However, it is by no way limited to visual maps, since the same technique is also applicable to maps of pose constraints (graph-SLAM) or any other kind of feature maps not relying on visual information.
The framework of Relative Bundle Adjustment (RBA) was introduced in a series of works by G. Sibley and colleagues:
- Sibley, G. Relative bundle adjustment. Department of Engineering Science, Oxford University, Tech. Rep, 2009. (PDF)
- Sibley, G. and Mei, C. and Reid, I. and Newman, P. Adaptive relative bundle adjustment. Robotics Science and Systems Conference. 2009. (PDF)
Sparser RBA (SRBA) is the name of the generic and extensible framework for RBA implemented in this C++ library, and introduced in:
- Blanco, J.L. and Gonzalez, J. and Fernandez-Madrigal, J.A. Sparser Relative Bundle Adjustment (SRBA): constant-time maintenance and local optimization of arbitrarily large maps, IEEE International Conference of Robotics and Automation (ICRA), 2013. (PDF), ICRA slides (PDF)
Documentation is now at the GitHub repository for MRPT/srba.
SRBA test 2: dataset_30k_1loop
SRBA test 3: linear trajectory with a stereo camera