Abstract
Multi-robot systems require collective map information on surrounding environments to efficiently cooperate with one another on assigned tasks. This paper addresses the problem of grid map merging to obtain the collective map information in multi-robot systems with unknown initial poses. If inter-robot measurements are not available, the only way to merge the maps is to find and match the overlapping area between maps. This paper proposes a tomographic feature-based map merging method, which can be successfully conducted with relatively small overlapping areas. The first part of the proposed method is to estimate a map transformation matrix using the Radon transform which can extract tomographically salient features from individual grid maps. The second part is to determine the search space using Gaussian mixture models based on the estimated map transformation matrix. The final part is to optimize an objective function modeled from tomographic information within the determined search space. Evaluation results with various pairs of individual maps produced by simulations and experiments showed that the proposed method can merge the individual maps more accurately than other map merging methods.
Highlights
Multi-robot systems have received attention in recent years because they have many advantages over single-robot systems such as time-efficiency and cost reduction [1]
This paper proposes a tomographic feature-based map merging method for multi-robot systems with unknown initial poses, which is categorized as indirect map merging
When multi-robot systems with unknown initial poses are utilized to explore unknown areas, each robot has built the individual map of the explored areas by the simultaneous localization and mapping (SLAM) technique with range or vision sensors
Summary
Multi-robot systems have received attention in recent years because they have many advantages over single-robot systems such as time-efficiency and cost reduction [1]. Since the inter-robot measurements included inevitable errors caused by imperfect sensors, the performance of the direct map merging depends on the system configuration to acquire the inter-robot measurements In another of our previous works [11], we proposed a grid map merging technique based on one-way observations, which reduced the conditions on rendezvous points. His method reduced computation time compared with the previous grid map matching techniques in a deterministic and non-iterative manner In another previous work [19], we proposed a variant of spectra-based map merging algorithm using virtual supporting lines, which was suitable for merging not grid maps but feature maps. This paper proposes a tomographic feature-based map merging method for multi-robot systems with unknown initial poses, which is categorized as indirect map merging.
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