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DynaFusion-SLAM: Multi-Sensor Fusion and Dynamic Optimization of Autonomous Navigation Algorithms for Pasture-Pushing

Zhiwei Liu1,2,3, Jiandong Fang1,2,3, Yudong Zhao2,3

  • 1College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, China.

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Summary

This study introduces a novel autonomous navigation system for robots in complex pasture environments, significantly improving mapping accuracy and path planning robustness through multi-sensor fusion. The system enhances operational efficiency and reduces navigation errors in challenging outdoor settings.

Keywords:
CartographerRTAB-Map algorithmautonomous navigationcrawler robotmulti-sensor fusionpasturepushing

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Area of Science:

  • Robotics
  • Computer Vision
  • Sensor Fusion

Background:

  • Existing autonomous navigation systems struggle with accuracy in complex outdoor environments like pastures.
  • Limited research exists on multi-sensor fusion algorithms for autonomous navigation in challenging agricultural settings.
  • Challenges include low fusion degrees and insufficient path accuracy for robotic operations.

Purpose of the Study:

  • To propose a multimodal autonomous navigation system using a loosely coupled Cartographer-RTAB-Map architecture.
  • To achieve high-precision mapping and robust path planning in complex pasture environments via laser-vision-inertial fusion.
  • To enhance the cruising accuracy and operational efficiency of robots in outdoor settings.

Main Methods:

  • Selected Cartographer as the front-end odometer due to its memory efficiency in large-scale scenarios.
  • Implemented a two-way position optimization mechanism involving Cartographer for mileage estimation and RTAB-Map for fusing depth camera point clouds and laser data.
  • Utilized Extended Kalman Filter (EKF) for fusing IMU and odometer data, Dijkstra's algorithm for global path planning, and Timed Elastic Band (TEB) for local path planning.

Main Results:

  • The proposed system significantly improved mapping accuracy in a simulated pasture, reducing maximum absolute error from 24.908 cm to 4.456 cm.
  • Multi-source odometry fusion effectively mitigated large-scale map offset and drift.
  • Navigation accuracy tests showed reduced Root Mean Square Error (RMSE) by 26.7% and Standard Deviation (Std) by 22.8% compared to AMCL.

Conclusions:

  • The multimodal autonomous navigation system demonstrates superior performance in complex simulated pasture environments.
  • The fusion of multi-sensor data and optimized localization mechanisms leads to enhanced mapping and navigation precision.
  • The system successfully enabled autonomous navigation for a robot, meeting task requirements for multi-point traversal and return.