Publication detail

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

PLASCENCIA, A. KARSTOFT, H.

Original Title

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

English Title

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

Type

journal article - other

Language

en

Original Abstract

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.

English abstract

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.

Keywords

Map Building; senzor fusion; Laser sensors; RGBD; Bayesian method

RIV year

2014

Released

07.03.2014

Publisher

NEUVEDEN

Location

NEUVEDEN

ISBN

2165-7866

Periodical

Journal of Information Technology & Software Engineering

Year of study

4

Number

1

State

US

Pages from

1

Pages to

7

Pages count

7

URL

Documents

BibTex


@article{BUT124201,
  author="Alfredo {Chavez Plascencia} and Henrik {Karstoft}",
  title="Map Building Based on a Xtion Pro Live RGBD and a Laser Sensors",
  annote="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.",
  address="NEUVEDEN",
  chapter="124201",
  doi="10.4172/2165-7866.1000126",
  edition="NEUVEDEN",
  howpublished="online",
  institution="NEUVEDEN",
  number="1",
  volume="4",
  year="2014",
  month="march",
  pages="1--7",
  publisher="NEUVEDEN",
  type="journal article - other"
}