ROS Navigation Tuning Guide
Not on the map. Why?
Abstract (arXiv)
The ROS navigation stack is powerful for mobile robots to move from place to place reliably. The job of navigation stack is to produce a safe path for the robot to execute, by processing data from odometry, sensors and environment map. Maximizing the performance of this navigation stack requires some fine tuning of parameters, and this is not as simple as it looks. One who is sophomoric about the concepts and reasoning may try things randomly, and wastes a lot of time. This article intends to guide the reader through the process of fine tuning navigation parameters. It is the reference when someone need to know the "how" and "why" when setting the value of key parameters. This guide assumes that the reader has already set up the navigation stack and ready to optimize it. This is also a summary of my work with the ROS navigation stack.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Jun 2017.
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Citing papers per year: 2020: 1; 2021: 3; 2022: 1; 2023: 1; 2024: 0; 2025: 0; 2026: 1.
7 papers.
- 2022Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigationcited by 27
- 2020APPLD: Adaptive Planner Parameter Learning from Demonstrationcited by 17
- 2021Towards Optimal Correlational Object Searchcited by 14
- 2021RoboCup@Home Education 2020 Best Performance: RoboBreizh, a modular approachcited by 0
- 2021An implementation of ROS Autonomous Navigation on Parallax Eddie platformcited by 0
- 2023Robot Skill Learning Via Classical Robotics-Based Generated Datasets: Advantages, Disadvantages, and Future Improvementcited by 0
- 2026APPLV: Adaptive Planner Parameter Learning from Vision-Language-Action Modelcited by 0
- 2026APPLV: Adaptive Planner Parameter Learning from Vision-Language-Action Model9 Mar 2026
- 2023Robot Skill Learning Via Classical Robotics-Based Generated Datasets: Advantages, Disadvantages, and Future Improvement20 Jan 2023
- 2022Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation22 Sep 2022
- 2021Towards Optimal Correlational Object Search19 Oct 2021
- 2021An implementation of ROS Autonomous Navigation on Parallax Eddie platform28 Aug 2021
- 2021RoboCup@Home Education 2020 Best Performance: RoboBreizh, a modular approach7 Jul 2021
- 2020APPLD: Adaptive Planner Parameter Learning from Demonstration31 Mar 2020