Evaluation of Topological Mapping Methods in Indoor Environments

Mapping is one of the key components of mobile robot navigation. Representing a map as a topological structure is suitable for fast path planning and does not require high positioning precision or high computational resources, which is particularly useful in large environments. In recent years, numerous methods of topological graph building have emerged. Most of these methods build a topological graph in conjunction with a metric map, and during tests and benchmarks, the performance of metric maps is typically assessed. However, topological graph quality is also crucial for robot navigation. In this study, we conducted an extensive evaluation of five open-source topological mapping methods (Hydra, S-graphs+, IncrementalTopo, ETPNav, and TSGM) using a large simulated indoor dataset with precise and noisy positioning. We used novel metrics to measure topological graph quality in our comparison. We found that all the methods except TGSM build an unconnected graph with both precise and noisy positioning. Hydra and S-graphs are more robust to position noise, but their graphs have a high percent of the edges crossing an obstacle. TSGM builds connected graphs, and it is not sensitive to positional noise because it does not use position as an input. However, its graphs have a high percent of the edges crossing an obstacle. IncrementalTopo has fewer edges passing through the obstacles; however, with noisy positioning, its graph becomes highly disconnected, which may also cause navigation failures. Thus, all the evaluated methods have their own advantages and drawbacks, and none of them builds a graph suitable for navigation out of the box. Overall, our study pinpoints the advantages and disadvantages of the state-of-the-art topological mapping methods and may aid researchers and practitioners in choosing a proper available algorithm for their specific setup.

Automated summary

This study evaluates five open-source topological mapping methods and compares them using novel metrics. The results show that each method has its own advantages and drawbacks, and none of them builds a graph suitable for navigation out of the box.