In this paper, we analyze and extend the recently proposed closed-form online pose-chain simultaneous localization and mapping (SLAM) algorithm. Pose-chains are a specific type of extremely sparse pose-graphs and a product of contemporary SLAM front-ends, which perform accurate visual odometry and reliable appearance-based loop detection. They are relevant for challenging robotic applications in large-scale 3-D environments for which frequent loop detection is not desired or not possible. Closed-form online pose-chain SLAM efficiently and accurately optimizes pose-chains by exploiting their Lie group structure. The convergence and optimality properties of this solution are discussed in detail and are compared against state-of-the-art iterative methods. We also provide a novel solution space, that of similarity transforms, which has not been considered earlier for the proposed algorithm. This allows for closed-form optimization of pose-chains that exhibit scale drift, which is important to monocular SLAM systems. On the basis of extensive experiments, specifically targeting 3-D pose-chains and using a total of 60 km of challenging binocular and monocular data, it is shown that the accuracy obtained by closed-form online pose-chain SLAM is comparable with that of state-of-the-art iterative methods, while the time it needs to compute its solution is orders of magnitudes lower. This novel SLAM technique thereby is relevant to a broad range of robotic applications and computational platforms.
COP-SLAM:Closed-form online pose-chain optimization for visual SLAM
2015-10-01
Dubbelman , G & Browning , B 2015 , ' COP-SLAM : Closed-form online pose-chain optimization for visual SLAM ' , IEEE Transactions on Robotics , vol. 31 , no. 5 , pp. 1194-1213 . https://doi.org/10.1109/TRO.2015.2473455
Article (Journal)
Electronic Resource
English
DDC: | 629 |
ExplORB-SLAM: Active Visual SLAM Exploiting the Pose-graph Topology
Springer Verlag | 2022
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