LOCALIZATION PERFORMANCE ESTIMATION WITH MULTI SENSOR FUSION

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2022-5-12

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Bingöl, Ulaş Süreyya

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Autonomous navigation gained significant attention as it is necessary for an over- growing use and integration of autonomous systems to numerous fields such as self- driving cars, logistics, and agricultural robots. These applications require robots to navigate from one location to another, which requires them to estimate their position accurately. Localization loss scenarios are instances when localization accuracy dete- riorates to a level that endangers the task. Reaching the false end goals, being stuck, traffic jams, and even crashes can occur when robots perform navigation with dete- riorated localization. Specifically for autonomous agents working alongside humans or as a fleet, localization accuracy is crucial. This work presents a novel method for monitoring and estimating localization per- formance of individual agents, called Localization Performance Analyzer (LPA). In contrast to the previous studies in this field, rather than specifically focusing on a few metrics to capture this deterioration, we instead investigate several metrics to find out which are more crucial for the estimation of localization failures. The algorithm first extracts a set of features from the robots’ sensors and its pose belief, and feeds them to a neural network (NN). Then, we train this network with the data from multiple robots, and able to predict whether the robot is lost or localized. We designed LPA in order to increase autonomy and to recover from localization divergences of a fleet of autonomous mobile robots (AMR). The effectiveness of the proposed algorithm is validated with the data gathered from industrial AMRs working in the same field.

Subject Keywords

Localization, Autonomous Mobile Robots, Autonomy

URI

https://hdl.handle.net/11511/97342

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

U. S. Bingöl, “LOCALIZATION PERFORMANCE ESTIMATION WITH MULTI SENSOR FUSION,” M.S. - Master of Science, Middle East Technical University, 2022.