Abstract
In the case of a single scene feature, the positioning of an indoor service robot takes a long time, and localization errors are likely to occur. A new method for a hybrid indoor localization system according to multi-sensor fusion is proposed to solve these problems. The localization process is divided in two stages: rough positioning and precise positioning. By virtue of the K nearest neighbors based on possibility (KNNBP) algorithm first created in the present study, the rough position of a robot is determined according to the received signal strength indicator (RSSI) of Wi-Fi. Then, the hybrid particle filter localization (HPFL) algorithm improved on the basis of adaptive Monte Carlo localization (AMCL) is adopted to get the precise localization, which integrates various information, including the rough position and information from Lidar, a compass, an occupancy grid map, and encoders. The experiments indicated that the positioning error was 0.05 m; the success rate of localization was 96% with even 3000 particles, and the global positioning time was 1.9 s. However, under the same conditions, the success rate of AMCL was approximately 40%, the required time was approximately 25.6 s, and the positioning accuracy was the same. This indicates that the hybrid indoor location system is efficient and accurate.
Highlights
It is required that an indoor service robot always accurately estimates its own position in order to complete its task efficiently
Experiments are presented to evaluate the performance of the hybrid indoor localization system compared with other methods
The precise localization was improved on the basis of adaptive Monte Carlo localization (AMCL) that is widely used in robot localization
Summary
It is required that an indoor service robot always accurately estimates its own position in order to complete its task efficiently. When in an environment with a low degree of distinction (e.g., a long corridor or hall), robot positioning is time-consuming and has low accuracy To solve these problems, numerous approaches that use inertial sensors, ultrasonic, radio-frequency (RF) ID tags, Wi-Fi, Lidar, vision sensors, and so on have been proposed [2,3,4,5,6,7,8]. The crucial disadvantage of these two approaches is that they bear a heavy online computational burden [12], and the bigger the working space is, the more serious the problem To solve this problem, Dellaert and Fox [13] introduced the adaptive Monte Carlo localization (AMCL) algorithm.
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