Long-term experiments with an adaptive spherical view representation for navigation in changing environments

Dayoub, Feras; Cielniak, Grzegorz; Duckett, Tom

doi:10.1016/j.robot.2011.02.013

Cited by 55 publications

(56 citation statements)

References 38 publications

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“…Also, we show two multisession experiments, 5 large scale sessions on Bicocca, and 36 sessions on a changing environment of an office floor at the University of Lincoln [4]. As front-end we use the BoW algorithm of [1], and as back-end we use the g2o framework, configured with the Gauss-Newton method and four iterations.…”

Section: Methodsmentioning

confidence: 99%

Robust Loop Closing Over Time

Latif¹,

Lerma²,

Neira³

2012

Robotics: Science and Systems VIII

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Abstract-Long term autonomy in robots requires the ability to reconsider previously taken decisions when new evidence becomes available. Loop closing links generated by a place recognition system may become inconsistent as additional evidence arrives. This paper is concerned with the detection and exclusion of such contradictory information from the map being built, in order to recover the correct map estimate. We propose a novel consistency based method to extract the loop closure regions that agree both among themselves and with the robot trajectory over time. We also assume that the contradictory loop closures are inconsistent among themselves and with the robot trajectory. We support our proposal, the RRR algorithm, on well-known odometry systems, e.g. visual or laser, using the very efficient graph optimization framework g2o as back-end. We back our claims with several experiments carried out on real data.

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Section: Methodsmentioning

confidence: 99%

Robust Loop Closing Over Time

Latif¹,

Lerma²,

Neira³

2012

Robotics: Science and Systems VIII

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“…where most representations use visual appearance for place recognition [13], [14]. However, these approaches present a decrease in robustness when facing long-term changes [15], as again they are prone to error when features appear and disappear over time. In [3], [4] dynamic models of the topological space that explicitly represent the environment changes and try to identify patterns by means of the Fourier transform are presented, for both localisation (node level) and navigation (transitions between nodes).…”

Section: Related Workmentioning

confidence: 99%

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

Krajník

Fentanes

Hanheide

et al. 2016

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

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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.

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“…We also found a very good example of long term navigation in the Lincoln dataset [5], 36 sessions on a changing environment of an office floor at the University of Lincoln. We run iRRR on this multi-session data.…”

Section: Long Term Operationmentioning

confidence: 69%

Robust loop closing over time for pose graph SLAM

Latif

Cadena

Neira

2013

The International Journal of Robotics Research

197

120

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Long term autonomous mobile robot operation requires considering place recognition decisions with great caution. A single incorrect decision that is not detected and reconsidered can corrupt the environment model that the robot is trying to build and maintain. This work describes a consensus-based approach to robust place recognition over time, that takes into account all the available information to detect and remove past incorrect loop closures. The main novelties of our work are: (1) the ability of realizing that, in light of new evidence, an incorrect past loop closing decision has been made; the incorrect information can be removed thus recovering the correct estimation with a novel algorithm; (2) extending our proposal to incremental operation; and (3) handling multi-session, spatially related or unrelated scenarios in a unified manner. We demonstrate our proposal, the RRR algorithm, on different odometry systems, e.g. visual or laser using different front-end loop closing techniques. For our experiments we use the efficient graph optimization framework g2o as back-end. We back our claims with several experiments carried out on real data, in single and multi-session experiments showing better results than those obtained by state of the art methods, comparisons against whom are also presented.

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Long-term experiments with an adaptive spherical view representation for navigation in changing environments

Cited by 55 publications

References 38 publications

Robust Loop Closing Over Time

Robust Loop Closing Over Time

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

Robust loop closing over time for pose graph SLAM

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