TL;DR: Sparser Relative Bundle Adjustment (SRBA) is a header-only C++ library for solving SLAM/BA in relative coordinates with flexibility for different submapping strategies and aimed at constant time local graph update. BSD 3-Clause License.
- Moreno, F.A. and Blanco, J.L. and Gonzalez, J. A constant-time SLAM back-end in the continuum between global mapping and submapping: application to visual stereo SLAM, International Journal of Robotics Research, 2016. (DOI), (Draft PDF)
- 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), BibTeX
- Blanco, J.L. User guide for libsrba: A generic C++ framework for Relative Bundle Adjustment (RBA) (PDF)
- MRPT >= 1.3.0 (web, github, Ubuntu PPA)
- gcc or clang (any version supported by Eigen 3) or MS Visual C++ 2008 or newer.
- CMake >=2.8
In Ubuntu, install requisites with:
sudo apt-get install build-essential cmake libmrpt-dev
Clone, configure and build as usual with CMake:
git clone https://github.com/MRPT/srba.git cd srba mkdir build && cd build cmake .. make make test
2. Theoretical bases
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 the ICRA 2013 paper (PDF, see full citation above).
3. Programming guide and documentation
4. Run sample datasets
4.1. Monocular visual SLAM with synthetic dataset
4.2. Relative 2D graph-SLAM
- Download and compile RWT, a small tool for generating synthetic datasets.
- Create an empty directory and copy there the files:
- datasets/srba-demos/world-2d-30k-rel-graph-slam.cfg (The configuration file for the synthetic dataset)
- datasets/srba-demos/world-2d-30k-landmarks.wrl (The geometrical description of the synthetic scenario)
- Generate the dataset, executing:
Now you can run RBA on the dataset with:
srba-slam --se2 --graph-slam -d dataset_30k_rel_graph_slam_SENSOR.txt \\ --submap-size 10 --max-spanning-tree-depth 3 --max-optimize-depth 3 \\ --verbose 1 --noise 0.001 --noise-ang 0.2 --add-noise \\ --gt-map dataset_30k_rel_graph_slam_GT_MAP.txt \\ --gt-path dataset_30k_rel_graph_slam_GT_PATH.txt # --step-by-step