Research of cartographer laser SLAM algorithm

Xu, Bo; Fu, Yiran; Zhang, Changsai; Liu, Zhengjun; Lv, Yueguang; Su, Jianzhong; Gong, Wei; Yang, Jian; Bao, Weimin; Chen, Weibiao; Shi, Zelin; Fei, Jindong; Han, Shensheng; Jin, Weiqi

doi:10.1117/12.2292864

Cited by 12 publications

(4 citation statements)

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“…Then, the accumulation of position errors (dead reckoning error) in the odometry during long-term and large-scale motion caused a deviation between the scene map and the actual environment [38,39]. For this problem, we would like to try other SLAM algorithm, such as Google’s Cartographer SLAM [40] instead of Gmapping SLAM.…”

Section: Experiments and Discussionmentioning

confidence: 99%

An Eight-Direction Scanning Detection Algorithm for the Mapping Robot Pathfinding in Unknown Indoor Environment

Jiang

Zhao

Dong³

et al. 2018

Sensors

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Aiming at the problem of how to enable the mobile robot to navigate and traverse efficiently and safely in the unknown indoor environment and map the environment, an eight-direction scanning detection (eDSD) algorithm is proposed as a new pathfinding algorithm. Firstly, we use a laser-based SLAM (Simultaneous Localization and Mapping) algorithm to perform simultaneous localization and mapping to acquire the environment information around the robot. Then, according to the proposed algorithm, the 8 certain areas around the 8 directions which are developed from the robot’s center point are analyzed in order to calculate the probabilistic path vector of each area. Considering the requirements of efficient traverse and obstacle avoidance in practical applications, the proposal can find the optimal local path in a short time. In addition to local pathfinding, the global pathfinding is also introduced for unknown environments of large-scale and complex structures to reduce the repeated traverse. The field experiments in three typical indoor environments demonstrate that deviation of the planned path from the ideal path can be kept to a low level in terms of the path length and total time consumption. It is confirmed that the proposed algorithm is highly adaptable and practical in various indoor environments.

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Section: Experiments and Discussionmentioning

confidence: 99%

An Eight-Direction Scanning Detection Algorithm for the Mapping Robot Pathfinding in Unknown Indoor Environment

Jiang

Zhao

Dong³

et al. 2018

Sensors

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show abstract

“…Each grid has two states: hit and miss. If the grid is hit, the adjacent grids are inserted into the hit set, and all relevant points on the connection line between the scan center and the scan point are added to the lost set [4]. Set a probability value for the grid that has not been observed before, and update the probability of the observed grid according to Eq.…”

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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“…Like the Hector SLAM, the Cartographer uses sensing data to correct the AUV motion. Accuracy sensing is required, but the other sensors of the AUV navigation system cannot be fully utilized [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Occupancy Grid-Based AUV SLAM Method with Forward-Looking Sonar

Guan

Zhou

et al. 2022

JMSE

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Simultaneous localization and mapping (SLAM) is an active localization method for Autonomous Underwater Vehicle (AUV), and it can mainly be used in unknown and complex areas such as coastal water, harbors, and wharfs. This paper presents a practical occupancy grid-based method based on forward-looking sonar for AUV. The algorithm uses an extended Kalman filter (EKF) to estimate the AUV motion states. First, the SLAM method fuses the data coming from the navigation sensors to predict the motion states. Subsequently, a novel particle swarm optimization genetic algorithm (PSO-GA) scan matching method is employed for matching the sonar scan data and grid map, and the matching pose would be used to correct the prediction states. Lastly, the estimated motion states and sonar scan data would be used to update the grid map. The experimental results based on the field data have validated that the proposed SLAM algorithm is adaptable to underwater conditions, and accurate enough to use for ocean engineering practical applications.

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Research of cartographer laser SLAM algorithm

Cited by 12 publications

References 0 publications

An Eight-Direction Scanning Detection Algorithm for the Mapping Robot Pathfinding in Unknown Indoor Environment

An Eight-Direction Scanning Detection Algorithm for the Mapping Robot Pathfinding in Unknown Indoor Environment

Design of Mobile Robot Based on Cartographer SLAM Algorithm

Occupancy Grid-Based AUV SLAM Method with Forward-Looking Sonar

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