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W-VSLAM: A Visual Mapping Algorithm for Indoor Inspection Robots.

Dingji Luo¹, Yucan Huang¹, Xuchao Huang¹

¹School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.

Sensors (Basel, Switzerland)

|September 14, 2024

Summary

This summary is machine-generated.

This study introduces a visual SLAM method integrating wheel odometry for indoor robots, significantly improving mapping accuracy. The enhanced visual simultaneous localization and mapping (SLAM) system achieves superior precision in robot pose estimation for autonomous navigation.

Keywords:

V-SLAM indoor inspection robots multi-sensor fusion

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Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

Indoor inspection robots require high-precision environmental perception for mapping.
Existing visual-inertial systems face inaccuracies from redundant pose degrees of freedom and accelerometer drift.

Purpose of the Study:

To develop a robust visual SLAM perception method integrating wheel odometry for indoor mobile robots.
To address inaccuracies in visual-inertial estimation for improved robotic mapping and localization.

Main Methods:

Parameterizing robot body pose in SE(2) and camera pose in SE(3).
Deriving visual and pose-constraint residuals and Jacobian matrices using pre-integration and marginalization theory.
Solving nonlinear optimization for optimal robot pose and landmark points.

Main Results:

The proposed method significantly outperforms ORBSLAM3 in perception accuracy.
Achieved root mean square error (RMSE) improvements of 89.2% in translation and 98.5% in rotation for absolute trajectory error (ATE).
Demonstrated overall trajectory localization accuracy between 5 and 17 cm.

Conclusions:

The integrated visual SLAM approach enhances perception accuracy for indoor mobile robots.
The method provides a reliable basis for preliminary mapping and path planning in autonomous navigation.
Validated effectiveness for precise localization in complex indoor environments.