Real-time indoor positioning using range imaging sensors

Kohoutek, Tobias K.; Mautz, Rainer; Donaubauer, Andreas

doi:10.1117/12.853688

Cited by 26 publications

(32 citation statements)

References 4 publications

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“…In Kohoutek et al (2010) it is shown how the indoor model provided by LoD4 of CityGML can be employed as a basis for positioning by image sensors.…”

Section: Indoor Navigationmentioning

confidence: 99%

CityGML – Interoperable semantic 3D city models

Gröger

Plümer

2012

ISPRS Journal of Photogrammetry and Remote Sensing

505

331

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“…In Kohoutek et al (2010) it is shown how the indoor model provided by LoD4 of CityGML can be employed as a basis for positioning by image sensors.…”

Section: Indoor Navigationmentioning

confidence: 99%

CityGML – Interoperable semantic 3D city models

Gröger

Plümer

2012

ISPRS Journal of Photogrammetry and Remote Sensing

505

331

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“…The technical characteristic comparison between the proposed method and stereo-vision based methods is presented as follows along with the results listed in Table 11:

The presented method can recognize more obstacle classes with higher efficiency than stereo-vision based methods. Based on one single frame, as shown in Table 11, the proposed method can recognize four types of obstacles while the methods reported in [23,28,29] can only one or two obstacles. In addition, the 3D information computing process is complex and the quality of the 3D information varies in different scenes.

…”

Section: Comparison and Discussionmentioning

confidence: 99%

“…Consequently, stereo-vision based methods are usually used for obstacle detection and simple classification. For example, the method in [28] can only detect rocks, and the methods in [23,29] can make a distinction between large rocks and small rocks. To improve the classification performance, some methods introduced more stereo frames.…”

Section: Comparison and Discussionmentioning

confidence: 99%

“…Microsoft Kinect is another kind of popular range camera based on the light coding technology. Its ability to fast and reliable access to 3D data at high frame rates has enormous potential applications in mobile robotic mapping [16–20], navigation [21,22] and localization [23], industrial robot collision avoidance [24,25], human tracking [26], special measurement [27]. …”

Section: Related Workmentioning

confidence: 99%

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Obstacle Classification and 3D Measurement in Unstructured Environments Based on ToF Cameras

Huang

Zhu

Wang

et al. 2014

Sensors

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Inspired by the human 3D visual perception system, we present an obstacle detection and classification method based on the use of Time-of-Flight (ToF) cameras for robotic navigation in unstructured environments. The ToF camera provides 3D sensing by capturing an image along with per-pixel 3D space information. Based on this valuable feature and human knowledge of navigation, the proposed method first removes irrelevant regions which do not affect robot's movement from the scene. In the second step, regions of interest are detected and clustered as possible obstacles using both 3D information and intensity image obtained by the ToF camera. Consequently, a multiple relevance vector machine (RVM) classifier is designed to classify obstacles into four possible classes based on the terrain traversability and geometrical features of the obstacles. Finally, experimental results in various unstructured environments are presented to verify the robustness and performance of the proposed approach. We have found that, compared with the existing obstacle recognition methods, the new approach is more accurate and efficient.

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“…The main criteria are the ac cessible coverage and accuracy of the position estimations. An overview of the latest positioning systems with a comparison of their specific coordinate accuracy and coverage is given in [3]. The coverage also contains the system's performance in non-Line-of sight (NLOS) scenarios.…”

Section: Introductionmentioning

confidence: 99%

Adding link quantity information to redundant RF signal strength estimates for improved indoor positioning

Fink

Beikirch

2012

2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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A common approach of range-based indoor posi tioning methods use the received signal strength (RSS) of RF packets from several anchor nodes to estimate their distances to a mobile emitter with unknown position. The range-based weighted centroid localization (WCL) approach with link quality information (LQI) is known to be more accurate than range free centroid localization (CL) methods using only a binary radio link information according to the cell of origin (COO) principle. With the combining of redundant RF channels using spatial and frequency diversity the WCL position estimation is proved to be even more reliable, although the RSS-based distance estimations of a single RF channel are known to be error-prone in multi path indoor environments. A novel range free approach using the exact number of available diversity channels -the link quantity information (LQnI) -is proposed. It needs no more infrastructural effort or processing power and can easily be applied to the range-based WCL estimation technique with redundant sensor information. Especially the combining of the redundant RSS-based distance estimations together with the LQnI approach leads to a more accurate position estimation. Experimental results in an office building and in a real-life tracking application for maintenance staff in the underground coal mining show the improvements of the additional range-free approach.

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Real-time indoor positioning using range imaging sensors

Cited by 26 publications

References 4 publications

CityGML – Interoperable semantic 3D city models

CityGML – Interoperable semantic 3D city models

Obstacle Classification and 3D Measurement in Unstructured Environments Based on ToF Cameras

Adding link quantity information to redundant RF signal strength estimates for improved indoor positioning

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