MDPI - Publisher of Open Access Journals

19 pages, 41334 KiB

Open AccessArticle

3D LiDAR Based SLAM System Evaluation with Low-Cost Real-Time Kinematics GPS Solution

by Stefan Hensel, Marin B. Marinov and Markus Obert

Computation 2022, 10(9), 154; https://doi.org/10.3390/computation10090154 - 4 Sep 2022

Cited by 5 | Viewed by 7788

Positioning mobile systems with high accuracy is a prerequisite for intelligent autonomous behavior, both in industrial environments and in field robotics. This paper describes the setup of a robotic platform and its use for the evaluation of simultaneous localization and mapping (SLAM) algorithms. A configuration using a mobile robot Husky A200, and a LiDAR (light detection and ranging) sensor was used to implement the setup. For verification of the proposed setup, different scan matching methods for odometry determination in indoor and outdoor environments are tested. An assessment of the accuracy of the baseline 3D-SLAM system and the selected evaluation system is presented by comparing different scenarios and test situations. It was shown that the hdl_graph_slam in combination with the LiDAR OS1 and the scan matching algorithms FAST_GICP and FAST_VGICP achieves good mapping results with accuracies up to 2 cm. Full article

(This article belongs to the Special Issue Applications of Statistics and Machine Learning in Electronics)

► Show Figures

Figure 1

13 pages, 16065 KiB

Open AccessArticle

Improved A* Path Planning Method Based on the Grid Map

by Yangqi Ou, Yuexin Fan, Xinglan Zhang, Yanhua Lin and Weijing Yang

Sensors 2022, 22(16), 6198; https://doi.org/10.3390/s22166198 - 18 Aug 2022

Cited by 45 | Viewed by 5248

Abstract

In obstacle spatial path planning, the traditional A* algorithm has the problem of too many turning points and slow search speed. With this in mind, a path planning method that improves the A* (A-Star) algorithm is proposed. The mobile robot platform was equipped with a lidar and inertial measurement unit (IMU). The Hdl_graph_slam mapping algorithm was used to construct a two-dimensional grid map, and the improved A* algorithm was used for path planning of the mobile robot. The algorithm introduced the path smoothing strategy and safety protection mechanism, and it eliminated redundant points and minimal corner points by judging whether there were obstacles in the connection of two path nodes. The algorithm effectively improved the smoothness of the path and facilitated the robot to move in the actual operation. It could avoid the wear of the robot by expanding obstacles and improving the safety performance of the robot. Subsequently, the algorithm introduced the steering cost model and the adaptive cost function to improve the search efficiency, making the search purposeful and effective. Lastly, the effectiveness of the proposed algorithm was verified by experiments. The average path search time was reduced by 13%. The average search extension node was reduced by 11%. The problems of too many turning points and slow search speed of traditional A* algorithm in path planning were improved. Full article

(This article belongs to the Section Sensing and Imaging)

► Show Figures

Figure 1

11 pages, 36244 KiB

Open AccessArticle

Performance of Different SLAM Algorithms for Indoor and Outdoor Mapping Applications

by Burak Akpınar

Appl. Syst. Innov. 2021, 4(4), 101; https://doi.org/10.3390/asi4040101 - 17 Dec 2021

Cited by 10 | Viewed by 5155

Abstract

Indoor and outdoor mapping studies can be completed relatively quickly, depending on the developments in Mobile Mapping Systems. Especially in indoor environments where high accuracy GNSS positions cannot be used, mapping studies can be carried out with SLAM algorithms. Although there are many different SLAM algorithms in the literature, each can produce results with different accuracy according to the mapped environment. In this study, 3D maps were produced with LOAM, A-LOAM, and HDL Graph SLAM algorithms in different environments such as long corridors, staircases, and outdoor environments, and the accuracies of the maps produced with different algorithms were compared. For this purpose, a mobile mapping platform using Velodyne VLP-16 LIDAR sensor was developed, and the odometer drift, which causes loss of accuracy in the data collected, was minimized by loop closure and plane detection methods. As a result of the tests, it was determined that the results of the LOAM algorithm were not as accurate as those of the A-LOAM and HDL Graph SLAM algorithms. Both indoor and outdoor environments and the A-LOAM results’ accuracy were two times better than HDL Graph SLAM results. Full article

► Show Figures

Figure 1

Search Results (3)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI

Saved Queries

Search Filter Reset All

Years

Feature Papers

Subjects

Journals

Article Types

Countries / Regions

Search Results (3)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI