A Compressed Sensing Approach for Multiple Obstacle Localisation Using Sonar Sensors in Air

A Compressed Sensing Approach for Multiple Obstacle Localisation Using Sonar Sensors in Air

Methods for autonomous navigation systems using sonars in air traditionally use the time-of-flight technique for obstacle detection and environment mapping. However, this technique suffers from constructive and destructive interference of ultrasonic reflections from multiple obstacles in the environ...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 20; no. 19; p. 5511
Main Authors: Tondin Ferreira Dias, Eduardo, Vieira Neto, Hugo, Schneider, Fábio Kurt
Format: Journal Article
Language:English
Published: Basel MDPI AG 26-09-2020
MDPI
Subjects:
Algorithms
Autonomous navigation
compressed sensing
environment mapping
Experiments
Inverse problems
Localization
Mapping
Matched pursuit
Methods
Navigation systems
Noise
Obstacle avoidance
Sensors
sonar imaging
Transducers
Ultrasonic imaging
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Summary:Methods for autonomous navigation systems using sonars in air traditionally use the time-of-flight technique for obstacle detection and environment mapping. However, this technique suffers from constructive and destructive interference of ultrasonic reflections from multiple obstacles in the environment, requiring several acquisitions for proper mapping. This paper presents a novel approach for obstacle detection and localisation using inverse problems and compressed sensing concepts. Experiments were conducted with multiple obstacles present in a controlled environment using a hardware platform with four transducers, which was specially designed for sending, receiving and acquiring raw ultrasonic signals. A comparison between the performance of compressed sensing using Orthogonal Matching Pursuit and two traditional image reconstruction methods was conducted. The reconstructed 2D images representing the cross-section of the sensed environment were quantitatively assessed, showing promising results for robotic mapping tasks using compressed sensing.
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These authors contributed equally to this work.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20195511