Evaluation of Modern Laser Based Indoor SLAM Algorithms

Krinkin, Kirill; Filatov, Antón; Filatov, Art yom; Huletski, Artur; Kartashov, Dmitriy

doi:10.23919/fruct.2018.8468263

Cited by 29 publications

(18 citation statements)

References 6 publications

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“…With the proposed VLP-constrained Gmapping process, the VLP-map and the generated SLAM map can be aligned and integrate together, so that the fusion between the VLP and LiDAR-SLAM is meaningful. It is worth to mention that, in this paper, we don't evaluate the map building accuracy, since the Gmapping is a related accurate map building method [47], [48]. The comparison of grid maps made through the traditional Gmapping (beginning of Point A and Point B in Fig5.…”

Section: B Map-fusion Performancementioning

confidence: 99%

Robot Localization and Navigation Using Visible Light Positioning and SLAM Fusion

Guan

Huang

Wen

et al. 2021

J. Lightwave Technol.

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Visible light positioning (VLP) is a promising technology since it can provide high accuracy indoor localization based on the existing lighting infrastructure. However, existing approaches often require dense LED distributions and persistent line-of-sight (LOS) between transmitter and receiver. What's more, sensors are imperfect, and their measurements are prone to errors. Through multi sensors fusion, we can compensate the deficiencies of stand-alone sensors and provide more reliable pose estimations. In this work, we propose a loosely-coupled multi-sensor fusion method based on VLP and Simultaneous Localization and Mapping (SLAM), using light detection and ranging (LiDAR), odometry, and rolling shutter camera. Our multi-sensor localizer can provide accurate and robust robot localization and navigation in LED shortage/outage situations. The experimental results show that our proposed scheme can provide an average accuracy of 2.5 cm with around 42 ms average positioning latency.

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Section: B Map-fusion Performancementioning

confidence: 99%

Robot Localization and Navigation Using Visible Light Positioning and SLAM Fusion

Guan

Huang

Wen

et al. 2021

J. Lightwave Technol.

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“…SLAM solves or reduces drift error of pose estimation by using an optimization technique using map data. This requires sensors which are able to sense the environment and object distances such as LiDAR or three-dimensional (3D) camera and sometimes additional sensors such as IMU encoders are needed [2,3]. The map is generated through the accumulation of scan data by this sensor during vehicle motion.…”

Section: Related Workmentioning

confidence: 99%

Cartographer SLAM Method for Optimization with an Adaptive Multi-Distance Scan Scheduler

et al. 2020

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This paper presents the use of Google's simultaneous localization and mapping (SLAM) technique, namely Cartographer, and adaptive multistage distance scheduler (AMDS) to improve the processing speed. This approach optimizes the processing speed of SLAM which is known to have performance degradation as the map grows due to a larger scan matcher. In this proposed work, the adaptive method was successfully tested in an actual vehicle to map roads in real time. The AMDS performs a local pose correction by controlling the LiDAR sensor scan range and scan matcher search window with the help of scheduling algorithms. The scheduling algorithms manage the SLAM that swaps between short and long distances during map data collection. As a result, the algorithms efficiently improved performance speed similar to short distance LiDAR scans while maintaining the accuracy of the full distance of LiDAR. By swapping the scan distance of the sensor, and adaptively limiting the search size of the scan matcher to handle difference scan sizes, the pose's generation performance time is improved by approximately 16% as compared with a fixed scan distance, while maintaining similar accuracy.

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“…Because of the observation noise of the sensor and the error of scanning matching itself, the pose map obtained by the front-end will be biased, so it needs to be corrected by the back-end graph optimization part. Back-end processing does not directly process the observation data of the sensor, but only optimizes the pose map created by the front-end, and obtains the maximum likelihood estimation of the pose, that is, the optimal pose sequence [2,3]. The algorithm framework is shown in Fig.…”

Section: Research On Cartographer Slam Algorithmmentioning

confidence: 99%

Design of Mobile Robot Based on Cartographer SLAM Algorithm

Qing¹,

Jia²,

Liu³

et al. 2019

dtcse

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Aiming at some problems existing in the development and industrialization of intelligent autonomous mobile robot, the SLAM mobile robot platform is designed and developed in this paper. Cartographer is used as the SLAM algorithm to realize the mapping and localization of the robot in the unknown environment. What's more, the new hardware design architecture is proposed, and the bottom control module is designed to be applied to the autonomous mobile robot. On the one hand, the design can reduce the workload of the non-algorithm part of the main control unit and improve the real-time of mapping. On the other hand, it solves the problem of communication clogging and enhances the compatibility of the system. Finally, an autonomous test platform is built to test the mapping and localization functions of the robot. The results of mapping, computational complexity and robustness of Cartographer SLAM and Hector SLAM algorithms are analyzed.

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Evaluation of Modern Laser Based Indoor SLAM Algorithms

Cited by 29 publications

References 6 publications

Robot Localization and Navigation Using Visible Light Positioning and SLAM Fusion

Robot Localization and Navigation Using Visible Light Positioning and SLAM Fusion

Cartographer SLAM Method for Optimization with an Adaptive Multi-Distance Scan Scheduler

Design of Mobile Robot Based on Cartographer SLAM Algorithm

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