A Quantitative Analysis on Gmapping Algorithm Parameters Based on Lidar in Small Area Environment

Wang, Hongyu; Huang, Mengxing; Wu, Di

doi:10.1007/978-981-32-9698-5_54

Cited by 2 publications

(3 citation statements)

References 10 publications

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“…Second, fine-tune parameters collectively, and the final results are evaluated visually. In [10], only two parameters were tuned in hybrid indoor and outdoor environment scenes, and the evaluation of CPU and Memory usage and visual inspection was presented. Other preceding works, such as [9][10][11][12][13][14], have tuned several specific parameters without International Journal of Intelligent Engineering and Systems, Vol.…”

Section: Introductionmentioning

confidence: 99%

“…In [10], only two parameters were tuned in hybrid indoor and outdoor environment scenes, and the evaluation of CPU and Memory usage and visual inspection was presented. Other preceding works, such as [9][10][11][12][13][14], have tuned several specific parameters without International Journal of Intelligent Engineering and Systems, Vol. 16 methodology to study and categorize all parameters functionality of Gmapping.…”

Section: Introductionmentioning

confidence: 99%

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An Extensive Analysis and Fine-Tuning of Gmapping’s Initialization Parameters

2023

IJIES

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Accurate localization and mapping are essential for autonomous navigation systems. The simultaneous localization and mapping (SLAM) algorithm, gmapping, is widely used for creating two-dimensional occupancy grid maps (2D-OGMs) due to its low cost and effectiveness in indoor environments. According to the SLAM interaction between localization and mapping terms, the evaluation of map accuracy implicitly means the evaluation of localization. This study focuses on improving the accuracy of the estimated 2D-OGMs by fine-tuning the 32 initialization parameters of gmapping using a turtlebot3-burger robot. An improved experimental procedure was developed, incorporating image registration and similarity measurement to evaluate the map accuracy. The results show a substantial improvement in map accuracy, from 78.84% to 94.18%. The study highlights the importance of fine-tuning the SLAM algorithm for improved map accuracy and provides valuable insights for developing autonomous navigation systems. The key contribution of this paper lies in the systematic classification, fine-tuning, and evaluation of the Gmapping initialization parameters.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Extensive Analysis and Fine-Tuning of Gmapping’s Initialization Parameters

2023

IJIES

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“…The ROS application programming interface is provided by most SLAM libraries. The most commonly used LiDAR SLAM libraries are Gmapping [7], Google Cartographers [8] and Hector SLAM [9]. Previous reports [10,11] include trajectory comparisons, which are measured via LiDAR-based SLAM trajectories with data obtained using various visible sensors (stereo and monocular cameras) and laser (depth sensors).…”

Section: Introductionmentioning

confidence: 99%

Cellular Communication-Based Autonomous UAV Navigation with Obstacle Avoidance for Unknown Indoor Environments

Choi¹,

Ramatryana²,

Shin³

2021

IJIES

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The technology of Unmanned Aerial Vehicles (UAV) has been the subject of pioneering research in recent years. Also, the subject has been used in various situations for both outdoor and indoor. In this paper, a cellular communication-based autonomous UAV Navigation is proposed that enables UAVs to maneuver independently in previously unknown and GPS-denied indoor environments with LiDAR sensors. The main idea of the proposed scheme is to implement LTE connection to UAV Navigation and compared it to a Wi-Fi connection. We have proposed that autonomous UAV navigation system for unknown environment like indoor. This system relies on the combination of ROS-based Hector SLAM systems, 2D-LiDAR sensor and LTE connection. The performance of proposed scheme is evaluated as the error distance and exploration time for unknown area like indoor environment. The mapping efficiency of LTE connection is 57.5% greater than the Wi-Fi connection. Computation time for flight is approximately 200 seconds.

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A Quantitative Analysis on Gmapping Algorithm Parameters Based on Lidar in Small Area Environment

Cited by 2 publications

References 10 publications

An Extensive Analysis and Fine-Tuning of Gmapping’s Initialization Parameters

An Extensive Analysis and Fine-Tuning of Gmapping’s Initialization Parameters

Cellular Communication-Based Autonomous UAV Navigation with Obstacle Avoidance for Unknown Indoor Environments

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