Lightweight Object-level Topological Semantic Mapping and Long-term Global Localization based on Graph Matching

Wang, Fan; Zhang, Chaofan; Tang, Fu-lin; Jiang, Hongkui; Wu, Yihong; Liu, Yong

doi:10.48550/arxiv.2201.05977

Cited by 3 publications

(6 citation statements)

References 20 publications

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“…Figure 16 visualizes the navigation environment. T1, T2, and T3 in Figure 16 correspond to the topological semantic maps of the three scenes, respectively, which are generated by our previous work [ 13 ]. G1, G2, and G3 in Figure 16 correspond to the high-precision occupancy grid maps of the navigation tasks respectively, which are generated by the OGMADWA method.…”

Section: Methodsmentioning

confidence: 99%

“…Inspired by the way humans navigate, we pay more attention to recent relative motion between objects other than the highly accurate global position for global navigation. Therefore, in this paper, referencing our previous work [ 13 ], we represent the environment by using a lightweight abstract topology graph that records the relative associations between objects. The basic structure of this map is a graph defined as

, where N and E denote the nodes and edges of the graph, respectively.…”

Section: Object-constrained Topological Global Path Searchingmentioning

confidence: 99%

“…( S1 – S3 ) show the test scenes of our method in the real world. ( T1 – T3 ) correspond to the topological semantic maps of the three scenes, respectively, which are generated by our previous work [ 13 ]. ( G1 – G3 ) correspond to the high-precision occupancy grid maps of the three scenes, respectively, which are generated by the OGMADWA method.…”

Section: Figurementioning

confidence: 99%

“…On the one hand, for global path searching, an object-constrained topological path-searching method is proposed. Firstly, a lightweight topological semantic map is constructed to reduce the dependence of visual navigation on a high-precision map, by modifying our previous work [ 13 ] with the graph memory and topological associations. Then, based on the constructed map, an object-based heuristic graph search method is proposed to effectively search global topological paths.…”

Section: Introductionmentioning

confidence: 99%

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Object-Based Reliable Visual Navigation for Mobile Robot

Wang

Zhang

et al. 2022

Sensors

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Visual navigation is of vital importance for autonomous mobile robots. Most existing practical perception-aware based visual navigation methods generally require prior-constructed precise metric maps, and learning-based methods rely on large training to improve their generality. To improve the reliability of visual navigation, in this paper, we propose a novel object-level topological visual navigation method. Firstly, a lightweight object-level topological semantic map is constructed to release the dependence on the precise metric map, where the semantic associations between objects are stored via graph memory and topological organization is performed. Then, we propose an object-based heuristic graph search method to select the global topological path with the optimal and shortest characteristics. Furthermore, to reduce the global cumulative error, a global path segmentation strategy is proposed to divide the global topological path on the basis of active visual perception and object guidance. Finally, to achieve adaptive smooth trajectory generation, a Bernstein polynomial-based smooth trajectory refinement method is proposed by transforming trajectory generation into a nonlinear planning problem, achieving smooth multi-segment continuous navigation. Experimental results demonstrate the feasibility and efficiency of our method on both simulation and real-world scenarios. The proposed method also obtains better navigation success rate (SR) and success weighted by inverse path length (SPL) than the state-of-the-art methods.

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Section: Methodsmentioning

confidence: 99%

, where N and E denote the nodes and edges of the graph, respectively.…”

Section: Object-constrained Topological Global Path Searchingmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Object-Based Reliable Visual Navigation for Mobile Robot

Wang

Zhang

et al. 2022

Sensors

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“…Let

be the subset of UAVs that covered q and the effective coverage by a subset of UAVs

in surveying

is then given by [ 26 , 27 ]

…”

Section: Problem Formulationmentioning

confidence: 99%

Research on Autonomous and Collaborative Deployment of Massive Mobile Base Stations in High-Rise Building Fire Field

Li,

Huang,

Liang

et al. 2023

Sensors

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High-rise building fires pose a serious threat to the lives and property safety of people. The lack of reliable and accurate positioning means is one of the main difficulties faced by rescuers. In the absence of prior knowledge of the high-rise building fire environment, the coverage deployment of mobile base stations is a challenging problem that has not received much attention in the literature. This paper studies the problem of the autonomous optimal deployment of base stations in high-rise building fire environments based on a UAV group. A novel problem formulation is proposed that solves the non-line-of-sight (NLOS) positioning problem in complex and unknown environments. The purpose of this paper is to realize the coverage and deployment of mobile base stations in complex and unknown fire environments. The NLOS positioning problem in the fire field environment is turned into the line-of-sight (LOS) positioning problem through the optimization algorithm. And there are more than three LOS base stations nearby at any point in the fire field. A control law which is formulated in a mathematically precise problem statement is developed that guarantees to meet mobile base stations’ deployment goals and to avoid collision. Finally, the positioning accuracy of our method and that of the common method were compared under many different cases. The simulation result showed that the positioning error of a simulated firefighter in the fire field environment was improved from more than 10 m (the positioning error of the traditional method) to less than 1 m.

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Collaborative Mobile Robotics for Semantic Mapping: A Survey

et al. 2022

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Ensuring safety in human–robot collaboration is one of the main challenges in mobile robotics today. Semantic maps are a potential solution because they provide semantic knowledge in addition to the geometric representation of the environment. They allow robots to perform their basic tasks using geometric representation, mainly localization, path planning and navigation, and additionally allow them to maintain a cognitive interpretation of the environment in order to reason and make decisions based on the context. The goal of this paper is to briefly review semantic mapping for a single mobile robot in indoor environments, and then focus on collaborative mobile semantic mapping. In both contexts, the semantic mapping process is divided into modules/tasks, and recent solutions for each module are discussed. Possible system architectures are also discussed for collaborative semantic mapping. Finally, future directions are highlighted.

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Lightweight Object-level Topological Semantic Mapping and Long-term Global Localization based on Graph Matching

Cited by 3 publications

References 20 publications

Object-Based Reliable Visual Navigation for Mobile Robot

Object-Based Reliable Visual Navigation for Mobile Robot

Research on Autonomous and Collaborative Deployment of Massive Mobile Base Stations in High-Rise Building Fire Field

Collaborative Mobile Robotics for Semantic Mapping: A Survey

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