Statistical Study of the Performance of Recursive Bayesian Filters with Abnormal Observations from Range Sensors

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 15; p. 4159
• Main Authors: Castellano-Quero, Manuel, Fernández-Madrigal, Juan-Antonio, García-Cerezo, Alfonso-José
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 26.07.2020; MDPI
• Subjects: abnormal observations; Bayesian analysis; Bayesian filters; Concrete; Filtration; Localization; mobile robots; Probabilistic methods; Random variables; range sensors; Robotics; Robots; Sensors; Statistical analysis; Statistical methods; Stochastic models; Theory
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20154159

Range sensors are currently present in countless applications related to perception of the environment. In mobile robots, these devices constitute a key part of the sensory apparatus and enable essential operations, that are often addressed by applying methods grounded on probabilistic frameworks such as Bayesian filters. Unfortunately, modern mobile robots have to navigate within challenging environments from the perspective of their sensory devices, getting abnormal observations (e.g., biased, missing, etc.) that may compromise these operations. Although there exist previous contributions that either address filtering performance or identification of abnormal sensory observations, they do not provide a complete treatment of both problems at once. In this work we present a statistical approach that allows us to study and quantify the impact of abnormal observations from range sensors on the performance of Bayesian filters. For that, we formulate the estimation problem from a generic perspective (abstracting from concrete implementations), analyse the main limitations of common robotics range sensors, and define the factors that potentially affect the filtering performance. Rigorous statistical methods are then applied to a set of simulated experiments devised to reproduce a diversity of situations. The obtained results, which we also validate in a real environment, provide novel and relevant conclusions on the effect of abnormal range observations in these filters.