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Multi-sensor person following in low-visibility scenarios
Jorge Sales1, Raúl Marín, Enric Cervera
1Department of Computer Science and Engineering, Jaume I University, Vicent Sos Baynat, s/n, 12071 Castellón, Spain. salesj@icc.uji.es
Sensors (Basel, Switzerland)
|December 14, 2011
Summary
This study introduces a robot navigation system using laser rangefinders and sonar sensors for low-visibility conditions, like smoky environments. It enables robots to follow people effectively in challenging scenarios where optical sensors fail.
Area of Science:
- Robotics
- Sensor Fusion
- Environmental Perception
Background:
- Person following with mobile robots is a key research area.
- Traditional methods using machine vision or laser rangefinders are limited in low-visibility conditions, such as smoky environments.
- Optical sensors are ineffective when visibility is compromised.
Purpose of the Study:
- To propose and compare alternative sensors and methods for person following in low visibility.
- To address the limitations of optical sensors in environments like those encountered in firefighting.
- To develop a robust person-following system for challenging conditions.
Main Methods:
- Utilizing laser rangefinders and sonar sensors as alternatives to purely optical sensors.
- Integrating a vision system capable of detecting and quantifying smoke levels.
- Developing a smoke detection algorithm to adapt sensor fusion based on environmental visibility.
Main Results:
- The proposed system effectively performs person following in simulated low-visibility conditions.
- Sensor fusion of laser rangefinders, sonar, and vision-based smoke detection enhances navigation robustness.
- The adaptive sensor combination strategy allows for continued operation where optical methods would fail.
Conclusions:
- The combination of laser rangefinders, sonar, and smoke-aware vision provides a viable solution for person following in low visibility.
- Adaptive sensor management based on environmental conditions is crucial for reliable robot navigation in hazardous scenarios.
- This approach significantly improves the applicability of mobile robots in environments like smoky firefighting settings.
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