Evaluation of RGB-D SLAM in Large Indoor Environments

Muravyev, Kirill; Yakovlev, Konstantin

doi:10.1007/978-3-031-23609-9_9

Cited by 3 publications

(3 citation statements)

References 33 publications

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“…However, while mapping a large environment over a long period of time, odometry error accumulation and map size growth may cause high memory consumption and inaccurate mapping. In work [8] two state-of-the-art metric mapping methods, RTAB-MAP and Voxgraph, were evaluated in a large indoor environment. After the robot traveled 1 km with an odometry drift, memory consumption for both methods exceeded 1 GB, and with RTAB-MAP, memory consumption exceeded 8 GB in some tests.…”

Section: Related Work a Mappingmentioning

confidence: 99%

“…Dense SLAM methods like RTAB-MAP [6] or Cartographer 1 store maps as occupancy grids, point clouds, or Oc-toMaps [7]. In large environments, these maps require large amounts of memory for storage, which may cause the robot's memory to overflow [8]. Additionally, as the map grows in size, the computational costs of map updating and path planning also increase.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Topological Mapping Methods in Indoor Environments

Muravyev,

Yakovlev

2023

IEEE Access

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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.

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Section: Related Work a Mappingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of Topological Mapping Methods in Indoor Environments

Muravyev,

Yakovlev

2023

IEEE Access

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“…6 In the case of mapping a large environment, such maps require a lot of memory for storage which may cause the robot's RAM to overflow. 7 Also, with the map size growth, the computational costs for its update also grow, which may cause the map update delays and mapping errors accumulation.…”

Section: Introductionmentioning

confidence: 99%

Maintaining topological maps for mobile robots

Muravyev,

Yakovlev

2024

Sixteenth International Conference on Machine Vision (ICMV 2023)

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Nowadays, mobile robots solve various tasks realted to navigation in an unknown or changing environment. In such conditions, mapping the environment is crucial for mobile robot navigation. Conventionally, maps are built as 2D or 3D dense metric structures which require much memory for storage and much computational time for path planning, especially in large environments. Representing a map as a topological structure (i.e. a graph of locations) allows fast path planning with low memory consumption. In this paper, we present a method of real-time topological map building and updating from odometry measurements and local point clouds. The proposed method guarantees the topological graph connectivity and adds shortcuts to the graph to optimize paths. We tested our method in a simulated environment and measured efficiency of path planning in the obtained graphs. In all the tests, the SPL value exceeded 81%, with 100% success rate. The source code of our method is available at https://github.com/KirillMouraviev/simple_toposlam_model.

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Enhancing Real-Time Visual SLAM with Distant Landmarks in Large-Scale Environments

Dou,

Zhao,

Liu

et al. 2024

Drones

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The efficacy of visual Simultaneous Localization and Mapping (SLAM) diminishes in large-scale environments due to challenges in identifying distant landmarks, leading to a limited perception range and trajectory drift. This paper presents a practical method to enhance the accuracy of feature-based real-time visual SLAM for compact unmanned vehicles by constructing distant map points. By tracking consecutive image features across multiple frames, remote map points are generated with sufficient parallax angles, extending the mapping scope to the theoretical maximum range. Observations of these landmarks from preceding keyframes are supplemented accordingly, improving back-end optimization and, consequently, localization accuracy. The effectiveness of this approach is ensured by the introduction of the virtual map point, a proposed data structure that links relational features to an imaginary map point, thereby maintaining the constrained size of local optimization during triangulation. Based on the ORB-SLAM3 code, a SLAM system incorporating the proposed method is implemented and tested. Experimental results on drone and vehicle datasets demonstrate that the proposed method outperforms ORB-SLAM3 in both accuracy and perception range with negligible additional processing time, thus preserving real-time performance. Field tests using a UGV further validate the efficacy of the proposed method.

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Evaluation of RGB-D SLAM in Large Indoor Environments

Cited by 3 publications

References 33 publications

Evaluation of Topological Mapping Methods in Indoor Environments

Evaluation of Topological Mapping Methods in Indoor Environments

Maintaining topological maps for mobile robots

Enhancing Real-Time Visual SLAM with Distant Landmarks in Large-Scale Environments

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