Detecting negative obstacle using Kinect sensor

A robot must have a good understanding of the environment for autonomous navigation. Mobile robot using fixed laser range scanner can only detect obstacle on a plane level. This may cause important obstacles not to be appropriately detected. This will cause the map generated to be inaccurate and col...

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Main Authors: Muhammad Fahmi Abdul Ghani, Khairul Salleh Mohamed Sahari
Format: Article
Language:English
Published: SAGE Publishing 2017-06-01
Series:International Journal of Advanced Robotic Systems
Online Access:https://doi.org/10.1177/1729881417710972
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spelling doaj-d15a237d23fe4a68ba421be1714864112020-11-25T03:03:21ZengSAGE PublishingInternational Journal of Advanced Robotic Systems1729-88142017-06-011410.1177/1729881417710972Detecting negative obstacle using Kinect sensorMuhammad Fahmi Abdul GhaniKhairul Salleh Mohamed SahariA robot must have a good understanding of the environment for autonomous navigation. Mobile robot using fixed laser range scanner can only detect obstacle on a plane level. This may cause important obstacles not to be appropriately detected. This will cause the map generated to be inaccurate and collision may actually occur during autonomous navigation. Microsoft Kinect is known to provide a low-cost 3-D data which can be used for mobile robot navigation. Many researchers focused on obstacles above ground level, and not negative obstacles such as holes or stairs. This article proposes the usage of Kinect sensor to detect negative obstacles and converts it into laser scan data. Positive obstacle is defined as the obstacle above the floor surface and negative obstacle is defined as the obstacle below the floor surface. Projection method is used to convert positive obstacle data from Kinect sensor to laser scan data. For negative obstacles detection, farthest point method and virtual floor projection method are used. The laser scan data from positive and negative obstacles are then combined to get an improved laser scan data, which includes all obstacles that are important for a robot to see. The negative obstacle detection methods are tested in simulated indoor environment and also experimental in a real environment. The simulation and experimental results have demonstrated the effectiveness of our proposed method to detect and map negative obstacles.https://doi.org/10.1177/1729881417710972
collection DOAJ
language English
format Article
sources DOAJ
author Muhammad Fahmi Abdul Ghani
Khairul Salleh Mohamed Sahari
spellingShingle Muhammad Fahmi Abdul Ghani
Khairul Salleh Mohamed Sahari
Detecting negative obstacle using Kinect sensor
International Journal of Advanced Robotic Systems
author_facet Muhammad Fahmi Abdul Ghani
Khairul Salleh Mohamed Sahari
author_sort Muhammad Fahmi Abdul Ghani
title Detecting negative obstacle using Kinect sensor
title_short Detecting negative obstacle using Kinect sensor
title_full Detecting negative obstacle using Kinect sensor
title_fullStr Detecting negative obstacle using Kinect sensor
title_full_unstemmed Detecting negative obstacle using Kinect sensor
title_sort detecting negative obstacle using kinect sensor
publisher SAGE Publishing
series International Journal of Advanced Robotic Systems
issn 1729-8814
publishDate 2017-06-01
description A robot must have a good understanding of the environment for autonomous navigation. Mobile robot using fixed laser range scanner can only detect obstacle on a plane level. This may cause important obstacles not to be appropriately detected. This will cause the map generated to be inaccurate and collision may actually occur during autonomous navigation. Microsoft Kinect is known to provide a low-cost 3-D data which can be used for mobile robot navigation. Many researchers focused on obstacles above ground level, and not negative obstacles such as holes or stairs. This article proposes the usage of Kinect sensor to detect negative obstacles and converts it into laser scan data. Positive obstacle is defined as the obstacle above the floor surface and negative obstacle is defined as the obstacle below the floor surface. Projection method is used to convert positive obstacle data from Kinect sensor to laser scan data. For negative obstacles detection, farthest point method and virtual floor projection method are used. The laser scan data from positive and negative obstacles are then combined to get an improved laser scan data, which includes all obstacles that are important for a robot to see. The negative obstacle detection methods are tested in simulated indoor environment and also experimental in a real environment. The simulation and experimental results have demonstrated the effectiveness of our proposed method to detect and map negative obstacles.
url https://doi.org/10.1177/1729881417710972
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