Supervised and Unsupervised Linear Learning Techniques for Visual Place Recognition in Changing Environments

Lowry, Stephanie; Milford, Michael

doi:10.1109/tro.2016.2545711

Cited by 54 publications

(56 citation statements)

References 43 publications

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“…Recent research showed that season-specific images can also be predicted using generative adversarial networks [49], [50]. Lowry & Milford [51] compare a similar appearance prediction technique with a change removal method and conclude that change removal is more robust and less data-intensive to train. Related work on laserbased localisation [52] uses long-term experience to learn error distributions for individual points in 3D point-cloud maps, which are then used during localisation to suppress the observations corresponding to map points with high errors.…”

Section: A Navigation and Mappingmentioning

confidence: 99%

Artificial Intelligence for Long-Term Robot Autonomy: A Survey

Kunze

Hawes

Duckett

et al. 2018

IEEE Robot. Autom. Lett.

158

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Abstract-Autonomous systems will play an essential role in many applications across diverse domains including space, marine, air, field, road, and service robotics. They will assist us in our daily routines and perform dangerous, dirty and dull tasks. However, enabling robotic systems to perform autonomously in complex, real-world scenarios over extended time periods (i.e. weeks, months, or years) poses many challenges. Some of these have been investigated by sub-disciplines of Artificial Intelligence (AI) including navigation & mapping, perception, knowledge representation & reasoning, planning, interaction, and learning. The different sub-disciplines have developed techniques that, when re-integrated within an autonomous system, can enable robots to operate effectively in complex, long-term scenarios. In this paper, we survey and discuss AI techniques as 'enablers' for long-term robot autonomy, current progress in integrating these techniques within long-running robotic systems, and the future challenges and opportunities for AI in long-term autonomy.

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Section: A Navigation and Mappingmentioning

confidence: 99%

Artificial Intelligence for Long-Term Robot Autonomy: A Survey

Kunze

Hawes

Duckett

et al. 2018

IEEE Robot. Autom. Lett.

158

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“…In the mapping and localisation community, the effects of the environment dynamics were studied mainly from the perspective of localisation reliability, which gradually deteriorates if the environment changes are neglected [18]. To deal with the changes, some approaches proposed to gradually adapt the maps by incrementally replacing their elements [4], by remapping the areas which changed [3], or by allowing multiple representations of the same location [2], by identifying the invariant characteristics of the world [19] or by general schemes to incrementally update continuous maps [20] using Bayesian techniques.…”

Section: Related Workmentioning

confidence: 99%

Time-varying Pedestrian Flow Models for Service Robots

Vintr¹,

Molina

Senanayake

et al. 2019

2019 European Conference on Mobile Robots (ECMR)

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We present a human-centric spatio-temporal model for service robots operating in densely populated environments for long time periods. The method integrates observations of pedestrians performed by a mobile robot at different locations and times into a memory efficient model, that represents the spatial layout of natural pedestrian flows and how they change over time. To represent temporal variations of the observed flows, our method does not model the time in a linear fashion, but by several dimensions wrapped into themselves. This representation of time can capture long-term (i.e. days to weeks) periodic patterns of peoples' routines and habits. Knowledge of these patterns allows making long-term predictions of future human presence and walking directions, which can support mobile robot navigation in human-populated environments. Using datasets gathered by a robot for several weeks, we compare the model to state-of-the-art methods for pedestrian flow modelling.

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“…The appearance variations impede the performance of visual place recognition, and many researchers are dedicated to mitigating the impact of appearance variations towards place recognition by different methods [8,9,12,18]. The illumination change is one of vital appearance variations, and quite a few place recognition algorithms [19,20] addressed the issue. Illumination invariant transformation was proposed to improve visual localization performance during daylight hours [19].…”

Section: State Of the Artmentioning

confidence: 99%

OpenMPR: Recognize places using multimodal data for people with visual impairments

Cheng

Wang

Bai

et al. 2019

Meas. Sci. Technol.

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Place recognition plays a crucial role in navigational assistance, and is also a challenging issue of assistive technology. The place recognition is prone to erroneous localization owing to various changes between database and query images. Aiming at the wearable assistive device for visually impaired people, we propose an open-sourced place recognition algorithm OpenMPR, which utilizes the multimodal data to address the challenging issues of place recognition.Compared with conventional place recognition, the proposed OpenMPR not only leverages multiple effective descriptors, but also assigns different weights to those descriptors in image matching. Incorporating GNSS data into the algorithm, the cone-based sequence searching is used for robust place recognition. The experiments illustrate that the proposed algorithm manages to solve the place recognition issue in the real-world scenarios and surpass the state-of-the-art algorithms in terms of assistive navigation performance. On the real-world testing dataset, the online OpenMPR achieves 88.7% precision at 100% recall without illumination changes, and achieves 57.8% precision at 99.3% recall with illumination changes. The OpenMPR is available at https://github.com/chengricky/OpenMultiPR.

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Supervised and Unsupervised Linear Learning Techniques for Visual Place Recognition in Changing Environments

Cited by 54 publications

References 43 publications

Artificial Intelligence for Long-Term Robot Autonomy: A Survey

Artificial Intelligence for Long-Term Robot Autonomy: A Survey

Time-varying Pedestrian Flow Models for Service Robots

OpenMPR: Recognize places using multimodal data for people with visual impairments

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