A survey of image semantics-based visual simultaneous localization and mapping: Application-oriented solutions to autonomous navigation of mobile robots

Xia, Linlin; Cui, Jiashuo; Shen, Ran; Xu, Xun; Gao, Yanyan; Li, Xinying

doi:10.1177/1729881420919185

Cited by 43 publications

(17 citation statements)

References 90 publications

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“…At the same time, for the needs of human-computer interaction, the problem of Visual SLAM is not only about pose positioning and construction of environmental consistency maps. Many practical applications of Visual SLAM such as robotic home care often require higher system accuracy, robustness, and a semantic map with perceptual information that can provide the robot with more higher-level environmental information and help complete complex interactive tasks [7].…”

Section: Slam (Simultaneous Localization and Mappingmentioning

confidence: 99%

The Method of Static Semantic Map Construction Based on Instance Segmentation and Dynamic Point Elimination

Zhang

Liu

et al. 2021

Electronics

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Semantic information usually contains a description of the environment content, which enables mobile robot to understand the environment and improves its ability to interact with the environment. In high-level human–computer interaction application, the Simultaneous Localization and Mapping (SLAM) system not only needs higher accuracy and robustness, but also has the ability to construct a static semantic map of the environment. However, traditional visual SLAM lacks semantic information. Furthermore, in an actual scene, dynamic objects will reduce the system performance and also generate redundancy when constructing map. these all directly affect the robot’s ability to perceive and understand the surrounding environment. Based on ORB-SLAM3, this article proposes a new Algorithm that uses semantic information and the global dense optical flow as constraints to generate dynamic-static mask and eliminate dynamic objects. then, to further construct a static 3D semantic map under indoor dynamic environments, a fusion of 2D semantic information and 3D point cloud is carried out. the experimental results on different types of dataset sequences show that, compared with original ORB-SLAM3, both Absolute Pose Error (APE) and Relative Pose Error (RPE) have been ameliorated to varying degrees, especially on freiburg3-walking-xyz, the APE reduced by 97.78% from the original average value of 0.523, and RPE reduced by 52.33% from the original average value of 0.0193. Compared with DS-SLAM and DynaSLAM, our system improves real-time performance while ensuring accuracy and robustness. Meanwhile, the expected map with environmental semantic information is built, and the map redundancy caused by dynamic objects is successfully reduced. the test results in real scenes further demonstrate the effect of constructing static semantic maps and prove the effectiveness of our Algorithm.

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Section: Slam (Simultaneous Localization and Mappingmentioning

confidence: 99%

The Method of Static Semantic Map Construction Based on Instance Segmentation and Dynamic Point Elimination

Zhang

Liu

et al. 2021

Electronics

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“…The navigation and location of mobile robots in uncertain environments are always the basic subjects of robot research. A lot of research and reviews have been undertaken to explore the simultaneous localization and mapping (SLAM) in mobile manipulation [ 6 , 32 , 33 , 34 ]. Kohlbrecher et al [ 35 ] described a Hector-SLAM-based [ 36 ] open-source SLAM system for urban search-and-rescue missions, which enables robots to map and locate themselves in a degraded urban environment, and independently explore disaster sites to identify victims and other interesting objects.…”

Section: Related Workmentioning

confidence: 99%

“…The adaptive Monte Carlo localization (AMCL) algorithm has been proved to be an efficient probabilistic localization method [ 5 ]. On the other hand, with the assistance of deep learning, more environmental information can be extracted from the vision sensors, and the robot’s ability to perceive the environment is enhanced [ 6 ]; thus, the accuracy of object detection has been greatly improved. Accordingly, mobile manipulation can provide highly flexible services and dexterous operations in structured and unstructured environments.…”

Section: Introductionmentioning

confidence: 99%

Mobile Manipulation Integrating Enhanced AMCL High-Precision Location and Dynamic Tracking Grasp

Zhou

Chou

Tuo

et al. 2020

Sensors

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Mobile manipulation, which has more flexibility than fixed-base manipulation, has always been an important topic in the field of robotics. However, for sophisticated operation in complex environments, efficient localization and dynamic tracking grasp still face enormous challenges. To address these challenges, this paper proposes a mobile manipulation method integrating laser-reflector-enhanced adaptive Monte Carlo localization (AMCL) algorithm and a dynamic tracking and grasping algorithm. First, by fusing the information of laser-reflector landmarks to adjust the weight of particles in AMCL, the localization accuracy of mobile platforms can be improved. Second, deep-learning-based multiple-object detection and visual servo are exploited to efficiently track and grasp dynamic objects. Then, a mobile manipulation system integrating the above two algorithms into a robotic with a 6-degrees-of-freedom (DOF) operation arm is implemented in an indoor environment. Technical components, including localization, multiple-object detection, dynamic tracking grasp, and the integrated system, are all verified in real-world scenarios. Experimental results demonstrate the efficacy and superiority of our method.

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“…Several recent surveys related to dynamic SLAM were reviewed, as shown in Table 1 . Xia et al [ 18 ] surveyed semantics-based V-SLAM. Chen et al [ 19 ] discussed the use of deep learning in SLAM.…”

Section: Introductionmentioning

confidence: 99%

A survey: which features are required for dynamic visual simultaneous localization and mapping?

Zheng

2021

Vis. Comput. Ind. Biomed. Art

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In recent years, simultaneous localization and mapping in dynamic environments (dynamic SLAM) has attracted significant attention from both academia and industry. Some pioneering work on this technique has expanded the potential of robotic applications. Compared to standard SLAM under the static world assumption, dynamic SLAM divides features into static and dynamic categories and leverages each type of feature properly. Therefore, dynamic SLAM can provide more robust localization for intelligent robots that operate in complex dynamic environments. Additionally, to meet the demands of some high-level tasks, dynamic SLAM can be integrated with multiple object tracking. This article presents a survey on dynamic SLAM from the perspective of feature choices. A discussion of the advantages and disadvantages of different visual features is provided in this article.

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A survey of image semantics-based visual simultaneous localization and mapping: Application-oriented solutions to autonomous navigation of mobile robots

Cited by 43 publications

References 90 publications

The Method of Static Semantic Map Construction Based on Instance Segmentation and Dynamic Point Elimination

The Method of Static Semantic Map Construction Based on Instance Segmentation and Dynamic Point Elimination

Mobile Manipulation Integrating Enhanced AMCL High-Precision Location and Dynamic Tracking Grasp

A survey: which features are required for dynamic visual simultaneous localization and mapping?

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