Article: Enhanced cartographer and TEB-based autonomous navigation for mobile robots in dynamic environments Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.34 pp.1 - 23 Abstract: This study addresses the challenge of autonomous navigation for intelligent mobile robots (IMRs) operating in dynamic environments by proposing a navigation framework that integrates an improved Google cartographer algorithm with a hybrid path planning strategy. The enhanced cartographer algorithm incorporates a KD-tree-based keypoint extraction technique for point cloud data, effectively reducing the amount of data required for point cloud matching to 10%-20% of the original volume. Furthermore, an adaptive loop closure detection mechanism is introduced, leading to a reduction of approximately 20% in mapping error. For path planning, a hybrid algorithm combining A* global planning with timed elastic band (TEB) local optimisation is developed. This approach dynamically adjusts the robot's pose sequence and time intervals, achieving a 98% success rate in obstacle avoidance while increasing path length by only 5%-10%. The planning cycle remains consistently within 100 ms. The proposed system demonstrates robust performance across practical scenarios, including warehouse logistics (with a 40% increase in handling efficiency) and medical delivery (achieving an 80% task completion rate). This research presents an efficient and scalable solution for autonomous navigation in complex dynamic environments, contributing both algorithmic innovation and significant engineering applicability. Inderscience Publishers - linking academia, business and industry through research

Title: Enhanced cartographer and TEB-based autonomous navigation for mobile robots in dynamic environments

Authors: Qi Chen; Xilong Qu; Xiao Tan; Siyang Yu; Guangjun Luo; Liqiang Tan

Addresses: College of Information Science and Engineering, Changsha Normal University, Changsha, 410100, China ' College of Information Science and Engineering, Changsha Normal University, Changsha, 410100, China ' School of Information Technology and Management, Hunan University of Finance and Economics, Changsha, 410205, China ' School of Information Technology and Management, Hunan University of Finance and Economics, Changsha, 410205, China ' College of Information Science and Engineering, Changsha Normal University, Changsha, 410100, China ' College of Information Science and Engineering, Changsha Normal University, Changsha, 410100, China

Abstract: This study addresses the challenge of autonomous navigation for intelligent mobile robots (IMRs) operating in dynamic environments by proposing a navigation framework that integrates an improved Google cartographer algorithm with a hybrid path planning strategy. The enhanced cartographer algorithm incorporates a KD-tree-based keypoint extraction technique for point cloud data, effectively reducing the amount of data required for point cloud matching to 10%-20% of the original volume. Furthermore, an adaptive loop closure detection mechanism is introduced, leading to a reduction of approximately 20% in mapping error. For path planning, a hybrid algorithm combining A* global planning with timed elastic band (TEB) local optimisation is developed. This approach dynamically adjusts the robot's pose sequence and time intervals, achieving a 98% success rate in obstacle avoidance while increasing path length by only 5%-10%. The planning cycle remains consistently within 100 ms. The proposed system demonstrates robust performance across practical scenarios, including warehouse logistics (with a 40% increase in handling efficiency) and medical delivery (achieving an 80% task completion rate). This research presents an efficient and scalable solution for autonomous navigation in complex dynamic environments, contributing both algorithmic innovation and significant engineering applicability.

Keywords: robot operating system; ROS; simultaneous localisation and mapping; SLAM; cartographer algorithm; adaptive loop closure detection; hybrid path planning; dynamic obstacle avoidance.

DOI: 10.1504/IJICT.2025.148821

International Journal of Information and Communication Technology, 2025 Vol.26 No.34, pp.1 - 23

Received: 15 Apr 2025
Accepted: 25 Jun 2025
Published online: 26 Sep 2025 *

Title: Enhanced cartographer and TEB-based autonomous navigation for mobile robots in dynamic environments

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