scholarly journals Optimized Landmark Distribution for Mobile Robot Visual Homing

Author(s):  
Xun Ji ◽  
Qidan Zhu
Keyword(s):  
Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3180 ◽  
Author(s):  
Xun Ji ◽  
Qidan Zhu ◽  
Junda Ma ◽  
Peng Lu ◽  
Tianhao Yan

Visual homing is an attractive autonomous mobile robot navigation technique, which only uses vision sensors to guide the robot to the specified target location. Landmark is the only input form of the visual homing approaches, which is usually represented by scale-invariant features. However, the landmark distribution has a great impact on the homing performance of the robot, as irregularly distributed landmarks will significantly reduce the navigation precision. In this paper, we propose three strategies to solve this problem. We use scale-invariant feature transform (SIFT) features as natural landmarks, and the proposed strategies can optimize the landmark distribution without over-eliminating landmarks or increasing calculation amount. Experiments on both panoramic image databases and a real mobile robot have verified the effectiveness and feasibility of the proposed strategies.


1999 ◽  
Vol 09 (05) ◽  
pp. 383-389 ◽  
Author(s):  
RALF MÖLLER

Insects of several species rely on visual landmarks for returning to important locations in their environment, The "average landmark vector model" is a parsimonious model which reproduces some aspects of the visual homing behavior of bees and ants, To gain insights in the structure and complexity of the neural apparatus that might underly the navigational capabilities of these animals, the average landmark vector model was implemented in analog hardware and used to control a mobile robot. The experiments demonstrate that the apparently complex task of visual homing might be realized by simple and mostly peripheral neural circuits in insect brains.


2016 ◽  
Vol 63 (9) ◽  
pp. 5523-5533 ◽  
Author(s):  
Anupa Sabnis ◽  
G. K. Arunkumar ◽  
Vikranth Dwaracherla ◽  
Leena Vachhani

2019 ◽  
Vol 139 (9) ◽  
pp. 1041-1050
Author(s):  
Hiroyuki Nakagomi ◽  
Yoshihiro Fuse ◽  
Hidehiko Hosaka ◽  
Hironaga Miyamoto ◽  
Takashi Nakamura ◽  
...  

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