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Related Experiment Video

Updated: Jul 9, 2025

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults

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Research on Positioning Accuracy of Mobile Robot in Indoor Environment Based on Improved RTABMAP Algorithm.

Shijie Zhou¹, Zelun Li¹, Zhongliang Lv¹

¹Chongqing University of Science and Technology, Chongqing 401331, China.

Sensors (Basel, Switzerland)

|December 9, 2023

Summary

This study introduces RTABMAP-VIWO, an enhanced visual simultaneous localization and mapping (VSLAM) method. It significantly reduces robot positioning errors indoors by fusing wheel odometry and IMU data, improving accuracy.

Keywords:

RTABMAP multi-sensor fusion robot localization visual SLAM

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Last Updated: Jul 9, 2025

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Area of Science:

Robotics
Computer Vision
Simultaneous Localization and Mapping (SLAM)

Background:

Visual SLAM is crucial for mobile robot positioning where GNSS fails.
Indoor robot navigation suffers from cumulative position errors due to environmental factors.
Existing methods like RTABMAP can experience decreasing accuracy over time.

Purpose of the Study:

To improve the accuracy and reduce cumulative errors in Visual SLAM for mobile robots.
To enhance the RTABMAP method by integrating Inertial Measurement Unit (IMU) data.
To evaluate the performance of the proposed RTABMAP-VIWO method against existing SLAM techniques.

Main Methods:

Proposed RTABMAP-VIWO method based on the RTABMAP framework.
Utilized an Extended Kalman Filter (EKF) to fuse wheel odometry and IMU attitude estimates.
Provided new prediction values to mitigate local cumulative errors.

Main Results:

RTABMAP-VIWO reduced Root-Mean-Square Error (RMSE) by 48.1% compared to RTABMAP on public datasets.
Achieved at least a 29.4% reduction in RMSE in real-world indoor experiments.
Demonstrated significant improvements in trajectory and posture error reduction.

Conclusions:

The RTABMAP-VIWO method effectively reduces cumulative errors in indoor mobile robot localization.
Incorporating IMU data into RTABMAP significantly enhances positioning accuracy.
The proposed method offers a feasible and more accurate solution for VSLAM in challenging indoor environments.