Abstract
The graph optimization has become the mainstream technology to solve the problems of SLAM (simultaneous localization and mapping). The pose graph in the graph based SLAM is consisted with a series of nodes and edges that connect the adjacent or related poses. With the widespread use of mobile robots, the scale of pose graph has rapidly increased. Therefore, optimizing a large-scale pose graph is the bottleneck of application of graph based SLAM. In this paper, we propose an optimization method basing on the decomposition of pose graph, of which we have noticed the sparsity. With the extraction of the Single-chain and the Parallel-chain, the pose graph is decomposed into many small subgraphs. Compared with directly processing the original graph, the speed of calculation is accelerated by separately optimizing the subgraph, which is because the computational complexity is increasing exponentially with the increase of the graph’s scale. This method we proposed is very suitable for the current multi-threaded framework adopted in the mainstream SLAM, which separately calculate the subgraph decomposed by our method, rather than the original optimization requiring a large block of time in once may cause CPU obstruction. At the end of the paper, our algorithm is validated with the open source dataset of the mobile robot, of which the result illustrates our algorithm can reduce the one-time resource consumption and the time consumption of the calculation with the same map-constructing accuracy.
Highlights
In order to make the mobile robot in unknown environment automatic movement, the robot need to construct environment model according to their own perception of environmental, so as to synchronous localization
The fundamental method of data fusion is based on a Bayes filter, which assume the current state is just influenced by the previous one
The algorithm we proposed in this paper has been coded with Matlab, which is separated to 4 parts, as talked above, to optimize the robot pose graph
Summary
In order to make the mobile robot in unknown environment automatic movement, the robot need to construct environment model according to their own perception of environmental, so as to synchronous localization. When the environment scale is increasing, the filtering method can hardly meet the needs of practical application. With the increasing of the environment scale, graph SLAM is widely concerned by academia and industry because it can solve global consistency solution of SLAM. Due to the large amount of data in global optimization, graph optimization can only be performed at the back end of SLAM, which can’t meet the need of practical application. The method based on the decomposition of pose graph’s sparsity is proposed in this paper, which accelerates the calculation speed of graph optimization. The equivalent substitution of the Single-chain and the Parallel-chain is the core method to decompose the pose graph into subgraph, so as to reduced that the scale of the information matrix of the overall graph.
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