Comparative analysis of ROS based 2D and 3D SLAM algorithms for Autonomous Ground Vehicles

P, Sankalprajan; Sharma, Thrilochan; Perur, Hamsa Datta; Pagala, Prithvi Sekhar

doi:10.1109/incet49848.2020.9154101

Cited by 14 publications

(3 citation statements)

References 17 publications

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“…The robot is manually controlled to navigate different environmental areas at a low and constant speed, ensuring repeated traversals to enhance the mapping accuracy through closed-loop detection of the SLAM algorithm. The FR-IQA methods used were Mean Square Error (MSE) [37], Peak Signal-to-Noise Ratio (PSNR) [38], and the Structural Similarity Index Measure (SSIM) [39]. The SSIM is a metric commonly used to compare two maps of the same size, for example, in terms of length and width.…”

Section: Evaluation Indexmentioning

confidence: 99%

Exploration- and Exploitation-Driven Deep Deterministic Policy Gradient for Active SLAM in Unknown Indoor Environments

Zhao,

Hwang

2024

Electronics

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This study proposes a solution for Active Simultaneous Localization and Mapping (Active SLAM) of robots in unknown indoor environments using a combination of Deep Deterministic Policy Gradient (DDPG) path planning and the Cartographer algorithm. To enhance the convergence speed of the DDPG network and minimize collisions with obstacles, we devised a unique reward function that integrates exploration and exploitation strategies. The exploration strategy allows the robot to achieve the shortest running time and movement trajectory, enabling efficient traversal of unmapped environments. Moreover, the exploitation strategy introduces active closed loops to enhance map accuracy. We conducted experiments using the simulation platform Gazebo to validate our proposed model. The experimental results demonstrate that our model surpasses other Active SLAM methods in exploring and mapping unknown environments, achieving significant grid completeness of 98.7%.

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Section: Evaluation Indexmentioning

confidence: 99%

Exploration- and Exploitation-Driven Deep Deterministic Policy Gradient for Active SLAM in Unknown Indoor Environments

Zhao,

Hwang

2024

Electronics

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“…Measurement and correction are then performed using LiDAR data and scan matching, followed by the weighting of samples. The final step is the map update and resampling method [22]. Highly precise LiDAR data is needed for mapping and pose correction via the scan-matching approach.…”

Section: 𝑝(𝑥mentioning

confidence: 99%

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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“…Utilizing an array of sensor data from cameras, LiDAR, and Inertial Measurement Units (IMUs), SLAM concurrently estimates sensor poses and fabricates a comprehensive three-dimensional representation of the surrounding milieu. This technology's prowess in real-time pose estimation has catalyzed its widespread adoption across various sectors of autonomous robotics, encompassing unmanned aerial vehicles [2], automated ground vehicles [3][4][5], and the burgeoning field of self-driving automobiles [6,7]. Moreover, SLAM's adeptness in real-time mapping plays a crucial role in robot navigation [8], enriching the experiences in virtual and augmented reality (VR/AR) [9] and bolstering the precision in surveying and mapping endeavors [10].…”

Section: Introductionmentioning

confidence: 99%

Improving SLAM Techniques with Integrated Multi-Sensor Fusion for 3D Reconstruction

Cai,

Ou,

Qin

2024

Sensors

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Simultaneous Localization and Mapping (SLAM) poses distinct challenges, especially in settings with variable elements, which demand the integration of multiple sensors to ensure robustness. This study addresses these issues by integrating advanced technologies like LiDAR-inertial odometry (LIO), visual-inertial odometry (VIO), and sophisticated Inertial Measurement Unit (IMU) preintegration methods. These integrations enhance the robustness and reliability of the SLAM process for precise mapping of complex environments. Additionally, incorporating an object-detection network aids in identifying and excluding transient objects such as pedestrians and vehicles, essential for maintaining the integrity and accuracy of environmental mapping. The object-detection network features a lightweight design and swift performance, enabling real-time analysis without significant resource utilization. Our approach focuses on harmoniously blending these techniques to yield superior mapping outcomes in complex scenarios. The effectiveness of our proposed methods is substantiated through experimental evaluation, demonstrating their capability to produce more reliable and precise maps in environments with variable elements. The results indicate improvements in autonomous navigation and mapping, providing a practical solution for SLAM in challenging and dynamic settings.

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Comparative analysis of ROS based 2D and 3D SLAM algorithms for Autonomous Ground Vehicles

Cited by 14 publications

References 17 publications

Exploration- and Exploitation-Driven Deep Deterministic Policy Gradient for Active SLAM in Unknown Indoor Environments

Exploration- and Exploitation-Driven Deep Deterministic Policy Gradient for Active SLAM in Unknown Indoor Environments

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

Improving SLAM Techniques with Integrated Multi-Sensor Fusion for 3D Reconstruction

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