Long-term topological localisation for service robots in dynamic environments using spectral maps

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Fentanes

Hanheide

et al. 2016

Self Cite

Abstract-We present a lifelong mapping and localisation system for long-term autonomous operation of mobile robots in changing environments. The core of the system is a spatiotemporal occupancy grid that explicitly represents the persistence and periodicity of the individual cells and can predict the probability of their occupancy in the future. During navigation, our robot builds temporally local maps and integrates then into the global spatio-temporal grid. Through re-observation of the same locations, the spatio-temporal grid learns the longterm environment dynamics and gains the ability to predict the future environment states. This predictive ability allows to generate time-specific 2d maps used by the robot's localisation and planning modules. By analysing data from a long-term deployment of the robot in a human-populated environment, we show that the proposed representation improves localisation accuracy and the efficiency of path planning. We also show how to integrate the method into the ROS navigation stack for use by other roboticists.

Section: Related Workmentioning

confidence: 99%

“…Previous work has shown that the predictive capabilities of the spectral models improve visual-based mobile robot localisation [3], navigation [4] and planning [5]. However, the previous works were aimed at proof-of-concept verification of the Frequency Map All authors are with the University of Lincoln, UK.…”

Section: Introductionmentioning

confidence: 99%

Persistent localization and life-long mapping in changing environments using the Frequency Map Enhancement

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Fentanes

Hanheide

et al. 2016

Self Cite

“…Sünderhauf and Neubert [34,35] mined a dictionary of superpixel-based visual-terms from long-term data and used this dictionary to translate between the appearance of given locations across seasons. Krajnik et al [36] used Fourier analysis to identify the cyclical changes of the environment states and showed that predicting these states for a particular time improves long-term localization [37].…”

Section: Visual Navigation In Changing Environmentsmentioning

confidence: 99%

Image features for visual teach-and-repeat navigation in changing environments

Robotics and Autonomous Systems

Cristóforis

Kusumam

et al. 2017

Self Cite

We present an evaluation of standard image features in the context of long-term visual teach-and-repeat navigation of mobile robots, where the environment exhibits significant changes in appearance caused by seasonal weather variations and daily illumination changes. We argue that for long-term autonomous navigation, the viewpoint-, scaleand rotation-invariance of the standard feature extractors is less important than their robustness to the mid-and longterm environment appearance changes. Therefore, we focus our evaluation on the robustness of image registration to variable lighting and naturally-occurring seasonal changes. We combine detection and description components of different image extractors and evaluate their performance on five datasets collected by mobile vehicles in three different outdoor environments over the course of one year. Moreover, we propose a trainable feature descriptor based on a combination of evolutionary algorithms and Binary Robust Independent Elementary Features, which we call GRIEF (Generated BRIEF). In terms of robustness to seasonal changes, the most promising results were achieved by the SpG/CNN and the STAR/GRIEF feature, which was slightly less robust, but faster to calculate.

“…Recently, some authors proposed to exploit these conflicting measurements in order to obtain information about the world dynamics and proposed representations that model the environment dynamics explicitly. These dynamic representations have shown their potential by improving mobile robot localization in changing environments [3], [4], [5], [6].…”

Section: Tkrajnik@lincolnacukmentioning

confidence: 99%

Life-long spatio-temporal exploration of dynamic environments

2015 European Conference on Mobile Robots (ECMR)

Santos

Duckett

2015

Self Cite

Abstract-We propose a new idea for life-long mobile robot spatio-temporal exploration of dynamic environments. Our method assumes that the world is subject to constant change, which adds an extra, temporal dimension to the explored space and makes the exploration task a never-ending data-gathering process. To create and maintain a spatio-temporal model of a dynamic environment, the robot has to determine not only where, but also when to perform observations. We address the problem by application of information-theoretic exploration to world representations that model the environment states' uncertainties as probabilistic functions of time.We compare the performance of different exploration strategies and temporal models on real-world data gathered over the course of several months and show that combination of dynamic environment representations with information-gain exploration principles allow to create and maintain up-to-date models of constantly changing environments.