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Ceiling vision-based active SLAM framework for dynamic and wide-open environments

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Abstract

A typical indoor environment can be divided into three categories; office (or room), hallway, and wide-open space such as lobby and hall. There have been numerous approaches for solving simultaneous localization and mapping (SLAM) problem in office (or room) and hallway. However, direct application of the existing approaches to wide-open space may be failed, because it has some distinguished features compared to other indoor places. To solve this problem, this paper proposes a new ceiling vision-based active SLAM framework, with an emphasis on practical deployment of service robot for commercial use in dynamically changing and wide-open environments by adopting the ceiling vision. First, for defining ceiling feature which can be extracted regardless of complexity of ceiling pattern we introduce a model-free landmark, i.e., visual node descriptor, which consists of edge points and their orientations in image space. Second, a recursive ‘explore and exploit’ is proposed for autonomous mapping. It is recursively performed by spreading out mapped area gradually while the robot is actively localized in the map. It can improve map accuracy due to frequent small loop closing. Third, a dynamic edge link (DEL) is proposed to cope with environmental changes in the map. Owing to DEL, we do not need to filter out corrupted sensor data and to distinguish moving object from static one. Also, a self-repairing map mechanism is introduced to deal with unexpected installation or removal of inner structures. We therefore achieve long-term navigation. Several simulations and real experiments in various places show that the proposed active SLAM framework could build a topologically consistent map, and demonstrated that it can be applied well to real environments such as wide-open space in a city hall and railway station.

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Authors and Affiliations

  1. Electronics and Telecommunications Research Institute (ETRI), Daegu, 711-883, Korea

    Su-Yong An

  2. Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Gyungbuk, 790-784, Korea

    Lae-Kyoung Lee & Se-Young Oh

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  1. Su-Yong An
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  2. Lae-Kyoung Lee
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Correspondence to Su-Yong An.

Electronic supplementary material

Appendix: manual node registration

Appendix: manual node registration

For various field application of the proposed algorithm, manual mapping is required as well as autonomous mapping. The reason behind the need for additional manual mapping is that we often need to locate a node at very specific position in order to enhance the robot’s navigation ability. This manual process is called manual node registration. The relations between nodes are also determined by the user, not by the Delaunay triangulation. The procedure for manual node registration is listed in Table 9. Through manual node registration, we can reduce the number of unnecessary nodes and edges and make a compact map specialized to the certain environment, hence improving the robot’s navigation ability. Figure 39 shows the example of the compact map made by manual node registration and its application to real environment.

Fig. 39
figure 39

The map constructed by manual node registration and its application to real navigation task. The map size is 21 \(\times \) 47 m (A grid is \(1~\times \) 1 m)

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Table 9 Procedure for manual node registration
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An, SY., Lee, LK. & Oh, SY. Ceiling vision-based active SLAM framework for dynamic and wide-open environments. Auton Robot 40, 291–324 (2016). https://doi.org/10.1007/s10514-015-9453-0

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