A Novel Approach for Lidar-Based Robot Localization in a Scale-Drifted Map Constructed Using Monocular SLAM

Wang, Su; Kobayashi, Yukinori; Ravankar, Ankit A.; Ravankar, Abhijeet; Emaru, Takanori

doi:10.3390/s19102230

Cited by 18 publications

(9 citation statements)

References 39 publications

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“…Zhang et al proposed a real-time method that shows better performance with fast-moving speed and intense illumination changes [16]. Wang et al suggested that using laser range to correct the scale drift of the visual [17].…”

Section: A Visual Slam Combination Of Different Sensorsmentioning

confidence: 99%

A Mobile Robot Visual SLAM System With Enhanced Semantics Segmentation

Liang

Chen

et al. 2020

IEEE Access

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Traditional visual simultaneous localization and mapping (SLAM) systems mostly based on small-area static environments. In recent years, some studies focused on combining semantic information with visual SLAM. However, most of them are hard to obtain better performance in the large-scale dynamic environment. And the accuracy, rapidity of the system still needs to strengthen. In this paper, we develop a more efficient semantic SLAM system in the two-wheeled mobile robot by using semantic segmentation to recognize people, chairs, and other objects in every keyframe. With a preliminary understanding of the environment, fusing the RGB-D camera and encoders information, to localization and creating a dense colored octree map without dynamic objects. Besides, for the incomplete identification of movable objects, we used image processing algorithms to enhance the semantic segmentation effect. In the proposed method, enhanced semantic segmentation in keyframes dramatically increases the efficiency of the system. Moreover, fusing the different sensors can highly raise localization accuracy. We conducted experiments on various datasets and in some real environments and compared them with DRE-SLAM, DS-SLAM, to evaluate the performance of the proposed approach. The results suggest we significantly improve the processing efficiency, robustness, and quality of the map.

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Section: A Visual Slam Combination Of Different Sensorsmentioning

confidence: 99%

A Mobile Robot Visual SLAM System With Enhanced Semantics Segmentation

Liang

Chen

et al. 2020

IEEE Access

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“…for heterogeneous robots with similar sensing modalities [8] or even different sensors [9], provided the robots move in a similar space. However, if the robots' trajectories never overlap, loop closures cannot help.…”

Section: Introductionmentioning

confidence: 99%

Any Way You Look at It: Semantic Crossview Localization and Mapping With LiDAR

Miller

Cowley²,

Konkimalla³

et al. 2021

IEEE Robot. Autom. Lett.

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Currently, GPS is by far the most popular global localization method. However, it is not always reliable or accurate in all environments. SLAM methods enable local state estimation but provide no means of registering the local map to a global one, which can be important for inter-robot collaboration or human interaction. In this work, we present a real-time method for utilizing semantics to globally localize a robot using only egocentric 3D semantically labelled LiDAR and IMU as well as top-down RGB images obtained from satellites or aerial robots. Additionally, as it runs, our method builds a globally registered, semantic map of the environment. We validate our method on KITTI as well as our own challenging datasets, and show better than 10 meter accuracy, a high degree of robustness, and the ability to estimate the scale of a top-down map on the fly if it is initially unknown.

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“…Hence, the adoption of the line-following methods for the wall-following behavior of a disinfection robot is not a feasible solution for larger environments and general public places. Many research studies have been conducted on navigating a robot in an environment by using Simultaneous Localization and Mapping (SLAM) techniques [ 19 , 20 ]. These methods are capable of navigating a robot in a given trajectory within an environment previously mapped.…”

Section: Introductionmentioning

confidence: 99%

Wall-Following Behavior for a Disinfection Robot Using Type 1 and Type 2 Fuzzy Logic Systems

Muthugala

Samarakoon

Rayguru

et al. 2020

Sensors

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Infectious diseases are caused by pathogenic microorganisms, whose transmission can lead to global pandemics like COVID-19. Contact with contaminated surfaces or objects is one of the major channels of spreading infectious diseases among the community. Therefore, the typical contaminable surfaces, such as walls and handrails, should often be cleaned using disinfectants. Nevertheless, safety and efficiency are the major concerns of the utilization of human labor in this process. Thereby, attention has drifted toward developing robotic solutions for the disinfection of contaminable surfaces. A robot intended for disinfecting walls should be capable of following the wall concerned, while maintaining a given distance, to be effective. The ability to operate in an unknown environment while coping with uncertainties is crucial for a wall disinfection robot intended for deployment in public spaces. Therefore, this paper contributes to the state-of-the-art by proposing a novel method of establishing the wall-following behavior for a wall disinfection robot using fuzzy logic. A non-singleton Type 1 Fuzzy Logic System (T1-FLS) and a non-singleton Interval Type 2 Fuzzy Logic System (IT2-FLS) are developed in this regard. The wall-following behavior of the two fuzzy systems was evaluated through simulations by considering heterogeneous wall arrangements. The simulation results validate the real-world applicability of the proposed FLSs for establishing the wall-following behavior for a wall disinfection robot. Furthermore, the statistical outcomes show that the IT2-FLS has significantly superior performance than the T1-FLS in this application.

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A Novel Approach for Lidar-Based Robot Localization in a Scale-Drifted Map Constructed Using Monocular SLAM

Cited by 18 publications

References 39 publications

A Mobile Robot Visual SLAM System With Enhanced Semantics Segmentation

A Mobile Robot Visual SLAM System With Enhanced Semantics Segmentation

Any Way You Look at It: Semantic Crossview Localization and Mapping With LiDAR

Wall-Following Behavior for a Disinfection Robot Using Type 1 and Type 2 Fuzzy Logic Systems

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