LiDAR SLAM with a Wheel Encoder in a Featureless Tunnel Environment

Filip, Iulian; Pyo, Juhyun; Lee, Meungsuk; Joe, Hangil

doi:10.3390/electronics12041002

Cited by 13 publications

(3 citation statements)

References 25 publications

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“…al. [10] demonstrated the improved performance in feature-less tunnel scenarios when combining wheel encoders. In [11], errors occurring in wheel encoders and IMU were learned and estimated, resulting in enhanced localization performance.…”

Section: Introductionmentioning

confidence: 95%

6-DOF Vehicle Pose Estimation Considering Lidar Odometry Initial Condition

Yang,

Huh

2024

IEEE Access

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Precise localization is essential for reliable autonomous driving. Traditionally, many systems have turned to lane level map matching techniques utilizing High Definition Maps (HD-Maps). However, it is necessary to have considerate efforts tied to the continuous availability and timeliness of these HD-Maps. Taking this into account, this paper explores an alternative approach, focusing on landmark-based odometry and a filtering-based technique. The proposed localization algorithm is integrated with the GPS and the odometry in a loosely coupled approach. Also, the initial odometry rotation is estimated to improve the global consistency in localization. In addition, this method considers the change in vehicle speed and updates the landmark-based odometry, aiming to address the specific concerns like repetitive geometry scenarios such as in tunnels. Through experimental tests in normal scenarios and challenging scenarios like tunnels, it is demonstrated that our method offers certain benefits over the existing techniques.

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Section: Introductionmentioning

confidence: 95%

6-DOF Vehicle Pose Estimation Considering Lidar Odometry Initial Condition

Yang,

Huh

2024

IEEE Access

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“…Simultaneous Localization and Mapping (SLAM) [1] is a set of approaches in which a Robot Operating System (ROS) robot [2] autonomously localizes itself and simultaneously maps the environment while navigating through it. Localization and mapping are important components of SLAM.…”

Section: Introductionmentioning

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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“…Localization and mapping are two important basic capabilities for achieving safe autonomous driving. SLAM technology can quickly map the field environment and effectively assist vehicles in completing automatic driving in complex settings [ 7 , 8 ]. Therefore, it is of great practical significance to carry out research on SLAM technology in complex field environments.…”

Section: Introductionmentioning

confidence: 99%

A Localization and Mapping Algorithm Based on Improved LVI-SAM for Vehicles in Field Environments

Han

Shi

Wang

2023

Sensors

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Quickly grasping the surrounding environment’s information and the location of the vehicle is the key to achieving automatic driving. However, accurate and robust localization and mapping are still challenging for field vehicles and robots due to the characteristics of emptiness, terrain changeability, and Global Navigation Satellite System (GNSS)-denied in complex field environments. In this study, an LVI-SAM-based lidar, inertial, and visual fusion using simultaneous localization and mapping (SLAM) algorithm was proposed to solve the problem of localization and mapping for vehicles in such open, bumpy, and Global Positioning System (GPS)-denied field environments. In this method, a joint lidar front end of pose estimation and correction was designed using the Super4PCS, Iterative Closest Point (ICP), and Normal Distributions Transform (NDT) algorithms and their variants. The algorithm can balance localization accuracy and real-time performance by carrying out lower-frequency pose correction based on higher-frequency pose estimation. Experimental results from the complex field environment show that, compared with LVI-SAM, the proposed method can reduce the translational error of localization by about 4.7% and create a three-dimensional point cloud map of the environment in real time, realizing the high-precision and high-robustness localization and mapping of the vehicle in complex field environments.

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LiDAR SLAM with a Wheel Encoder in a Featureless Tunnel Environment

Cited by 13 publications

References 25 publications

6-DOF Vehicle Pose Estimation Considering Lidar Odometry Initial Condition

6-DOF Vehicle Pose Estimation Considering Lidar Odometry Initial Condition

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

A Localization and Mapping Algorithm Based on Improved LVI-SAM for Vehicles in Field Environments

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