2A2-M06 Vision based Localization and Mapping for Indoor Robots using RGBD Sensor

2015 ◽  
Vol 2015 (0) ◽  
pp. _2A2-M06_1-_2A2-M06_3
Author(s):  
Ankit Ravankar ◽  
Abhijeet Ravankar ◽  
Yukinori Kobayashi ◽  
Lv Jixin ◽  
Takanori Emaru
2019 ◽  
Vol 9 (7) ◽  
pp. 1428 ◽  
Author(s):  
Ran Wang ◽  
Xin Wang ◽  
MingMing Zhu ◽  
YinFu Lin

Autonomous underwater vehicles (AUVs) are widely used, but it is a tough challenge to guarantee the underwater location accuracy of AUVs. In this paper, a novel method is proposed to improve the accuracy of vision-based localization systems in feature-poor underwater environments. The traditional stereo visual simultaneous localization and mapping (SLAM) algorithm, which relies on the detection of tracking features, is used to estimate the position of the camera and establish a map of the environment. However, it is hard to find enough reliable point features in underwater environments and thus the performance of the algorithm is reduced. A stereo point and line SLAM (PL-SLAM) algorithm for localization, which utilizes point and line information simultaneously, was investigated in this study to resolve the problem. Experiments with an AR-marker (Augmented Reality-marker) were carried out to validate the accuracy and effect of the investigated algorithm.


2019 ◽  
Vol 38 (14) ◽  
pp. 1549-1559 ◽  
Author(s):  
Maxime Ferrera ◽  
Vincent Creuze ◽  
Julien Moras ◽  
Pauline Trouvé-Peloux

We present a new dataset, dedicated to the development of simultaneous localization and mapping methods for underwater vehicles navigating close to the seabed. The data sequences composing this dataset are recorded in three different environments: a harbor at a depth of a few meters, a first archeological site at a depth of 270 meters, and a second site at a depth of 380 meters. The data acquisition is performed using remotely operated vehicles equipped with a monocular monochromatic camera, a low-cost inertial measurement unit, a pressure sensor, and a computing unit, all embedded in a single enclosure. The sensors’ measurements are recorded synchronously on the computing unit and 17 sequences have been created from all the acquired data. These sequences are made available in the form of ROS bags and as raw data. For each sequence, a trajectory has also been computed offline using a structure-from-motion library in order to allow the comparison with real-time localization methods. With the release of this dataset, we wish to provide data difficult to acquire and to encourage the development of vision-based localization methods dedicated to the underwater environment. The dataset can be downloaded from: http://www.lirmm.fr/aqualoc/


Author(s):  
F. Menna ◽  
A. Torresani ◽  
E. Nocerino ◽  
M. M. Nawaf ◽  
J. Seinturier ◽  
...  

<p><strong>Abstract.</strong> Metrology is fundamental in all the applications that require to qualify, verify and validate measured data according to standards or, in other words, to assess their compliance with predefined tolerances. At sea, metrology is commonly associated with the process of measuring underwater structures, mainly pipeline elements widely used in offshore industry. Subsea operations are very expensive; optimizing time and money resources are the core factors driving innovation in the subsea metrology industry. In this study, the authors investigate the use of state-of-art vision-based algorithms, i.e. ORB-SLAM2 and Visual Odometry, as a navigation tool to assist and control a Remotely Operated Vehicle (ROV) while performing subsea metrology operations. In particular, the manuscript will focus on methods for assessing the accuracy of both trajectory and tie points provided by the tested approaches and evaluating whether the preliminary real time reconstruction meets the tolerances defined in typical subsea metrology scenarios.</p>


Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1326
Author(s):  
Jatin Upadhyay ◽  
Abhishek Rawat ◽  
Dipankar Deb ◽  
Vlad Muresan ◽  
Mihaela-Ligia Unguresan

A robotic navigation system operates flawlessly under an adequate GPS signal range, whereas indoor navigation systems use the simultaneous localization and mapping system or other vision-based localization systems. The sensor used in indoor navigation systems is not suitable for low power and small scale robotic systems. The wireless area network transmitting devices have fixed transmission power, and the receivers get the different values of signal strength based on their surrounding environments. In the proposed method, the received signal strength index (RSSI) values of three fixed transmitter units are measured every 1.6 m in mesh format and analyzed by the classifiers, and robot position can be mapped in the indoor area. After navigation, the robot analyzes objects and detects and recognize human faces with the help of object recognition and facial recognition-based classification methods respectively. This robot detects the intruder with the current position in an indoor environment.


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