Map Building Based on a Xtion Pro Live RGBD and a Laser Sensors

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F14%3APU118877" target="_blank" >RIV/00216305:26230/14:PU118877 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.omicsgroup.org/journals/map-building-based-on-a-xtion-pro-live-rgbd-and-a-laser-sensors-2165-7866.1000126.pdf" target="_blank" >http://www.omicsgroup.org/journals/map-building-based-on-a-xtion-pro-live-rgbd-and-a-laser-sensors-2165-7866.1000126.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.4172/2165-7866.1000126" target="_blank" >10.4172/2165-7866.1000126</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Map Building Based on a Xtion Pro Live RGBD and a Laser Sensors

Popis výsledku v původním jazyce

The main contribution of this paper is to show the feasibility to use the novel Xtion Pro Live RGBD camera into the field of sensor data fusion and map making based on the well established Bayesian method. This approach involves the combination of the Xtion Pro Live RGBD camera with the Hokuyo laser sensor data readings, which are interpreted by a probabilistic heuristic model that abstracts the beam into a ray casting to an occupied grid cell. Occupancy grid is proposed for representing the probability of the occupied and empty areas. In order to update the occupancy grid, the Bayesian estimation method is applied to both sensor data arrays. The sensor data fusion yields a significant improvement of the combined occupancy grid compared to the individual occupied sensor data readings. It is also shown by the Mahalanobis distance that by integrating both sensors, more reliable and accurate maps are produced. The approach has been exemplified by following a sensor data fusion method to building a map of an indoor environment robot.

Název v anglickém jazyce

Map Building Based on a Xtion Pro Live RGBD and a Laser Sensors

Popis výsledku anglicky

The main contribution of this paper is to show the feasibility to use the novel Xtion Pro Live RGBD camera into the field of sensor data fusion and map making based on the well established Bayesian method. This approach involves the combination of the Xtion Pro Live RGBD camera with the Hokuyo laser sensor data readings, which are interpreted by a probabilistic heuristic model that abstracts the beam into a ray casting to an occupied grid cell. Occupancy grid is proposed for representing the probability of the occupied and empty areas. In order to update the occupancy grid, the Bayesian estimation method is applied to both sensor data arrays. The sensor data fusion yields a significant improvement of the combined occupancy grid compared to the individual occupied sensor data readings. It is also shown by the Mahalanobis distance that by integrating both sensors, more reliable and accurate maps are produced. The approach has been exemplified by following a sensor data fusion method to building a map of an indoor environment robot.

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Information Technology & Software Engineering

ISSN

2165-7866

e-ISSN

—

Svazek periodika

4

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

7

Strana od-do

1-7

Kód UT WoS článku

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EID výsledku v databázi Scopus

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