Likelihood Estimation For Slam Using Kinect Device
In the world of robotics, problems of visual SLAM requires an understanding of loop-closure detection and global localization, having said that in order to perform mapping and localization simultaneously we should be able to efficiently recognize an environment that has been previously visited usi...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English English |
| Published: |
IJEAST
2016
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/13912/1/24-29%2CTesma105%2CIJEAST.pdf http://umpir.ump.edu.my/id/eprint/13912/7/fim-2016-sorooshian-Likelihood%20Estimation%20For%20Slam1.pdf http://umpir.ump.edu.my/id/eprint/13912/ http://www.ijeast.com/papers/24-29,Tesma105,IJEAST.pdf |
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| Summary: | In the world of robotics, problems of visual SLAM
requires an understanding of loop-closure detection and global localization, having said that in order to perform mapping and localization simultaneously we should be able to efficiently recognize an environment that has been previously visited using the current data from our RGBD camera. In this paper
we present an online method to recognize and generate
information regarding a previously visited place using the
visual bag of words model which relies on Bayesian filtering to
calculate the probability of loop closure. We would also
demonstrate the robustness and effectiveness of our method by
real time loop closure detection for an indoor image sequence
using Microsoft Kinect camera |
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