Map building with RGB-D camera for mobil robot

Somlyai, Laszlo; Vamossy, Zoltan

doi:10.1109/ines.2012.6249883

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…In case of further measurements, it is maximum 20 mm. A modified average filter can reduce this fault to 3 mm [39]. The few meters' detection proximity of the sensor makes it able to be used on smaller sized robots indoors.…”

Section: Methodsmentioning

confidence: 99%

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ISVD-Based Advanced Simultaneous Localization and Mapping (SLAM) Algorithm for Mobile Robots

Somlyai

Vamossy

2022

Machines

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In the case of simultaneous localization and mapping, route planning and navigation are based on data captured by multiple sensors, including built-in cameras. Nowadays, mobile devices frequently have more than one camera with overlapping fields of view, leading to solutions where depth information can also be gathered along with ordinary RGB color data. Using these RGB-D sensors, two- and three-dimensional point clouds can be recorded from the mobile devices, which provide additional information for localization and mapping. The method of matching point clouds during the movement of the device is essential: reducing noise while having an acceptable processing time is crucial for a real-life application. In this paper, we present a novel ISVD-based method for displacement estimation, using key points detected by SURF and ORB feature detectors. The ISVD algorithm is a fitting procedure based on SVD resolution, which removes outliers from the point clouds to be fitted in several steps. The developed method removes these outlying points in several steps, in each iteration examining the relative error of the point pairs and then progressively reducing the maximum error for the next matching step. An advantage over relevant methods is that this method always gives the same result, as no random steps are included.

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Section: Methodsmentioning

confidence: 99%

“…As the RGB-D sensor produces more noisy measurements for larger distances, feature points more than 5.5 meters away were discarded during the matching procedure based on our previous results [39]. Therefore, set F n only contains these pre-filtered feature points.…”

Section: Methodsmentioning

confidence: 99%

ISVD-Based Advanced Simultaneous Localization and Mapping (SLAM) Algorithm for Mobile Robots

Somlyai

Vamossy

2022

Machines

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Autonomous Navigation of Mobile Robot Using Kinect Sensor

Zainuddin

Mustafah

Shawgi

et al. 2014

2014 International Conference on Computer and Communication Engineering

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The problem of achieving real time process in depth camera application, in particular when used for indoor mobile robot localization and navigation is far from being solved. Thus, this paper presents autonomous navigation of the mobile robot by using Kinect sensor. By using Microsoft Kinect XBOX 360 as the main sensor, the robot is expected to navigate and avoid obstacles safely. By using depth data, 3D point clouds, filtering and clustering process, the Kinect sensor is expected to be able to differentiate the obstacles and the path in order to navigate safely. Therefore, this research requirement to propose a creation of low-cost autonomous mobile robot that can be navigated safely.

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Improvement of an adaptive fuzzy-based obstacle avoidance algorithm using virtual and real kinect sensors

Csaba

2013

2013 IEEE 9th International Conference on Computational Cybernetics (ICCC)

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Map building with RGB-D camera for mobil robot

Cited by 6 publications

References 10 publications

ISVD-Based Advanced Simultaneous Localization and Mapping (SLAM) Algorithm for Mobile Robots

ISVD-Based Advanced Simultaneous Localization and Mapping (SLAM) Algorithm for Mobile Robots

Autonomous Navigation of Mobile Robot Using Kinect Sensor

Improvement of an adaptive fuzzy-based obstacle avoidance algorithm using virtual and real kinect sensors

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