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Laser-based pedestrian tracking in outdoor environments by multiple mobile robots.

Masataka Ozaki¹, Kei Kakimuma, Masafumi Hashimoto

¹Graduate School of Doshisha University, Tatara, Kyotanabe, Kyoto 6100321, Japan. mantuuman@gmail.com

Sensors (Basel, Switzerland)

|December 4, 2012

Summary

This study introduces a cooperative, decentralized outdoor pedestrian tracking system using mobile robots. The system enhances tracking accuracy and robustness by sharing data among robots, outperforming individual tracking methods.

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Area of Science:

Robotics
Computer Vision
Sensor Fusion

Background:

Accurate pedestrian tracking is crucial for autonomous systems operating in dynamic outdoor environments.
Existing centralized tracking systems face limitations in scalability and robustness.

Purpose of the Study:

To develop and evaluate a decentralized, cooperative laser-based pedestrian tracking system for outdoor environments.
To improve tracking performance and robustness compared to individual robot tracking.

Main Methods:

Utilizing a group of mobile robots equipped with laser scanners for pedestrian detection via occupancy grids.
Implementing Kalman filtering and global-nearest-neighbor (GNN) for individual robot tracking.
Employing real-time-kinematic GPS (RTK-GPS) and laser scan matching for robot pose estimation.

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Broadcasting and fusing tracking data among robots using the covariance intersection (CI) method for cooperative tracking.

Main Results:

Cooperative tracking demonstrated superior performance compared to conventional individual tracking methods in simulations and experiments.
The decentralized architecture, without a central server, offers enhanced scalability and robustness.
Individual robots can track pedestrians even when they are temporarily out of another robot's sensor range.

Conclusions:

The proposed cooperative tracking system effectively enhances outdoor pedestrian tracking capabilities.
Decentralized, multi-robot cooperation provides a scalable and robust solution for autonomous navigation and surveillance.
This approach significantly improves situational awareness in complex, dynamic environments.