Paper Info
Title
Learning State-Dependent, Sensor Measurement Models For Localization
Abstract
A robot typically relies on sensor measurements to infer its state and the state of its environment. Unfortunately, sensor measurements are noisy, and the amount of noise can vary with state. The literature provides a collection of methods that estimate and adapt measurement noise over time. However, many methods do not assume that measurement noise is stochastic, or they do not estimate sensor measurement bias and noise based on state. In this paper, we propose a novel method called state-dependent, sensor measurement models (SDSMMs). This method: 1) learns to estimate measurement probability density functions directly from sensor measurements and 2) stochastically estimates an expected measurement (which includes measurement bias) and a measurement noise, both of which are conditioned upon the states of a robot and its environment. Throughout this paper, we discuss how to learn an SDSMM and use it with the Extended Kalman Filter (EKF). We then apply our method to solve an EKF localization problem using a real robot dataset. Our localization results showed that at least one of our proposed methods outperformed a standard EKF in all 15 cases for 2D position error and 10 of 15 cases for 1D orientation error. Our methods had a mean improvement of 39% for position and 15% for orientation.
Year
DOI
Venue
2019
10.1109/IROS40897.2019.8968185
2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
Field
DocType
ISSN
Computer vision,State dependent,Extended Kalman filter,Computer science,Position error,Artificial intelligence,Robot,Probability density function
Conference
2153-0858
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Troi Williams100.34
Yu Sun220835.82