Dynamic Prediction-Based Optical Localization of a Robot During Continuous Movement

Author(s):  
Jason N. Greenberg ◽  
Xiaobo Tan

Abstract Localization of mobile robots is essential for navigation and data collection. This work presents an optical localization scheme for mobile robots during the robot’s continuous movement, despite that only one bearing angle can be captured at a time. In particular, this paper significantly improves upon our previous works where the robot has to pause its movement in order to acquire the two bearing angle measurements needed for position determination. The latter restriction forces the robot to work in a stop-and-go mode, which constrains the robot’s mobilitty. The proposed scheme exploits the velocity prediction from Kalman filtering, to properly correlate two consecutive measurements of bearing angles with respect to the base nodes (beacons) to produce location measurement. The proposed solution is evaluated in simulation and its advantage is demonstrated through the comparison with the traditional approach where the two consecutive angle measurements are directly used to compute the location.

Author(s):  
Benjamin Abruzzo ◽  
David Cappelleri ◽  
Philippos Mordohai

Abstract This paper presents and evaluates a relative localization scheme for a heterogeneous team of low-cost mobile robots. An error-state, complementary Kalman Filter was developed to fuse analytically-derived uncertainty of stereoscopic pose measurements of an aerial robot, made by a ground robot, with the inertial/visual proprioceptive measurements of both robots. Results show that the sources of error, image quantization, asynchronous sensors, and a non-stationary bias, were sufficiently modeled to estimate the pose of the aerial robot. In both simulation and experiments, we demonstrate the proposed methodology with a heterogeneous robot team, consisting of a UAV and a UGV tasked with collaboratively localizing themselves while avoiding obstacles in an unknown environment. The team is able to identify a goal location and obstacles in the environment and plan a path for the UGV to the goal location. The results demonstrate localization accuracies of 2cm to 4cm, on average, while the robots operate at a distance from each-other between 1m and 4m.


IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 39690-39701 ◽  
Author(s):  
Guoxing Bai ◽  
Li Liu ◽  
Yu Meng ◽  
Weidong Luo ◽  
Qing Gu ◽  
...  

2016 ◽  
Vol 61 (4) ◽  
pp. 1105-1110 ◽  
Author(s):  
Che Lin ◽  
Zhiyun Lin ◽  
Ronghao Zheng ◽  
Gangfeng Yan ◽  
Guoqiang Mao

2020 ◽  
Vol 4 (6) ◽  
pp. 1-4
Author(s):  
Jifeng Zou ◽  
Yimao Sun ◽  
Qun Wan

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