SLAMBench is a benchmark suite for visual SLAM – simultaneous localisation and mapping, the key problem in understanding the 3D world in real time. SLAM is central to many applications in robotics, autonomous vehicles, and augemented and virtual reality. SLAMBench enables users to test different SLAM algorithms and to evaluate them with respect to accuracy, performance and power consumption – thus enabling users to map the landscape of SLAM solutions to find the right approach for their application context.
SLAMBench began with just the KinectFusion algorithm for dense volumetric SLAM, and has been extended (SLAMBench 2 and SLAMBench 3) to incorporate more algorithms, on more platforms, with more sophisticated and more automated evaluation and design space exploration.
SLAMBench is the result of an extended collaboration, and is presented in this article:
Mihai Bujanca, Paul Gafton, Sajad Saeedi, Andy Nisbet, Bruno Bodin, Michael F. P. O’Boyle, Andrew J. Davison, Paul H. J. Kelly, Graham D. Riley, Barry Lennox, Mikel Luján, Steve B. Furber: SLAMBench 3.0: Systematic Automated Reproducible Evaluation of SLAM Systems for Robot Vision Challenges and Scene Understanding. ICRA 2019: 6351-6358