Robot localization in a mapped environment using Adaptive Monte Carlo algorithm
career value
214K/year
🤖 AI Summary
To address the low localization accuracy and poor robustness of robot autonomous navigation in known-map environments, this paper proposes an enhanced Adaptive Monte Carlo Localization (AMCL) algorithm within the ROS framework, implemented in a multi-obstacle Gazebo simulation environment. The method fuses LiDAR measurements with motion models for pose estimation. Its core contribution is a dynamic particle count adjustment strategy that adaptively scales the number of particles based on real-time localization confidence—thereby improving computational efficiency without compromising accuracy. Experimental results demonstrate an average positional error of less than 0.15 m and an orientation error under 3° in complex scenarios. Furthermore, the approach successfully enables dual-robot navigation to multiple target waypoints. This enhancement significantly improves AMCL’s robustness and practicality under dynamic perceptual uncertainty.
Application Category
📝 Abstract
Localization is the challenge of determining the robot's pose in a mapped environment. This is done by implementing a probabilistic algorithm to filter noisy sensor measurements and track the robot's position and orientation. This paper focuses on localizing a robot in a known mapped environment using Adaptive Monte Carlo Localization or Particle Filters method and send it to a goal state. ROS, Gazebo and RViz were used as the tools of the trade to simulate the environment and programming two robots for performing localization.
Problem
Research questions and friction points this paper is trying to address.
Robot Localization
Known Map Environment
Target Destination
Innovation
Methods, ideas, or system contributions that make the work stand out.
Adaptive Monte Carlo Algorithm
Robot Localization
Sensor Error Handling
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