Building Metric-Topological Map to Efficient Object Search for Mobile Robot

Author(s):  
Ying Zhang ◽  
Guohui Tian ◽  
Xuyang Shao ◽  
Shaopeng Liu ◽  
Mengyang Zhang ◽  
...  
Author(s):  
Mehdi Dehghani ◽  
Hamed Kharrati ◽  
Hadi Seyedarabi ◽  
Mahdi Baradarannia

The accumulated error and noise sensitivity are the two common problems of ordinary inertial sensors. An accurate gyroscope is too expensive, which is not normally applicable in low-cost missions of mobile robots. Since the accelerometers are rather cheaper than similar types of gyroscopes, using redundant accelerometers could be considered as an alternative. This mechanism is called gyroscope-free navigation. The article deals with autonomous mobile robot (AMR) navigation based on gyroscope-free method. In this research, the navigation errors of the gyroscope-free method in long-time missions are demonstrated. To compensate the position error, the aid information of low-cost stereo cameras and a topological map of the workspace are employed in the navigation system. After precise sensor calibration, an amendment algorithm is presented to fuse the measurement of gyroscope-free inertial measurement unit (GFIMU) and stereo camera observations. The advantages and comparisons of vision aid navigation and gyroscope-free navigation of mobile robots will be also discussed. The experimental results show the increasing accuracy in vision-aid navigation of mobile robot.


2011 ◽  
Vol 2011 (0) ◽  
pp. _1P1-M06_1-_1P1-M06_4
Author(s):  
Puwanan Chumtong ◽  
Yasushi Mae ◽  
Kenichi Ohara ◽  
Tomohito Takubo ◽  
Tatsuo Arai

1999 ◽  
Author(s):  
Goksel Dedeoglu ◽  
Maja J. Mataric ◽  
Gaurav S. Sukhatme

2021 ◽  
Vol 12 (3) ◽  
pp. 134
Author(s):  
Farzin Foroughi ◽  
Zonghai Chen ◽  
Jikai Wang

Deep learning has made great advances in the field of image processing, which allows automotive devices to be more widely used in humans’ daily lives than ever before. Nowadays, the mobile robot navigation system is among the hottest topics that researchers are trying to develop by adopting deep learning methods. In this paper, we present a system that allows the mobile robot to localize and navigate autonomously in the accessible areas of an indoor environment. The proposed system exploits the Convolutional Neural Network (CNN) model’s advantage to extract data feature maps for image classification and visual localization, which attempts to precisely determine the location region of the mobile robot focusing on the topological maps of the real environment. The system attempts to precisely determine the location region of the mobile robot by integrating the CNN model and topological map of the robot workspace. A dataset with small numbers of images is acquired from the MYNT EYE camera. Furthermore, we introduce a new loss function to tackle the bounded generalization capability of the CNN model in small datasets. The proposed loss function not only considers the probability of the input data when it is allocated to its true class but also considers the probability of allocating the input data to other classes rather than its actual class. We investigate the capability of the proposed system by evaluating the empirical studies based on provided datasets. The results illustrate that the proposed system outperforms other state-of-the-art techniques in terms of accuracy and generalization capability.


2018 ◽  
Vol 3 (4) ◽  
pp. 3081-3088 ◽  
Author(s):  
Chaoqun Wang ◽  
Jiyu Cheng ◽  
Jiankun Wang ◽  
Xintong Li ◽  
Max Q.-H. Meng
Keyword(s):  
Road Map ◽  

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