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Insect-controlled Robot: A Mobile Robot Platform to Evaluate the Odor-tracking Capability of an Insect

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Consensus-Based Information Filtering in Distributed LiDAR Sensor Network for Tracking Mobile Robots.

Isabella Luppi¹, Neel Pratik Bhatt¹, Ehsan Hashemi¹

¹Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada.

Sensors (Basel, Switzerland)

|May 11, 2024

Summary

This summary is machine-generated.

This study introduces a novel framework for mobile robot state estimation and tracking using distributed LiDAR sensors. It improves accuracy in dynamic, low-visibility, and occlusion scenarios, enhancing robot navigation reliability.

Keywords:

LiDAR-based state estimation consensus filters distributed sensor networks information filters perception

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Area of Science:

Robotics
Sensor Networks
State Estimation

Background:

Mobile robots operate in dynamic environments with sensor limitations.
Accurate state estimation is crucial for reliable navigation and tracking.
LiDAR sensor networks offer potential for enhanced environmental perception.

Purpose of the Study:

To design a distributed state observer for mobile robot state estimation and tracking.
To enhance 3D bounding box detection and tracking in challenging conditions.
To improve state prediction accuracy in low-visibility and occlusion scenarios.

Main Methods:

Developed a novel framework using a consensus-based information filter.
Integrated a region of interest for state estimation.
Utilized remote sensing for dynamic process input identification.
Employed a switching consensus information filter with stationary LiDAR sensor data.

Main Results:

The framework demonstrated enhanced 3D bounding box detection and tracking.
State prediction accuracy was improved in low-visibility and occlusion scenarios.
Experimental evaluations confirmed accuracy and computational efficiency.
Reduced estimation error in terms of mean square and covariance.

Conclusions:

The proposed distributed state observer framework effectively enhances mobile robot state estimation and tracking.
Integrating stationary LiDAR sensor data improves tracking reliability and reduces estimation errors.
The framework offers a robust solution for mobile robots in dynamic and occluded environments.