Visual Place Recognition: A Survey

Lowry, Stephanie; Sünderhauf, Niko; Newman, Paul; Leonard, John J.; Cox, David D.; Corke, Peter; Milford, Michael

doi:10.1109/tro.2015.2496823

Cited by 965 publications

(673 citation statements)

References 199 publications

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“…Recently, Ros et al, in [92], present the challenges of Visual SLAM for driverless cars: building long-life maps, how to share maps between vehicles and the necessity to work on high-level features to ease recognition. A very complete survey on visual place recognition has also been published lately [93]. The authors go through the different modules that are essential in this field: image processing (descriptors, etc.…”

Section: Relevant Surveys and Existing Data Setsmentioning

confidence: 99%

See 1 more Smart Citation

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Bresson

Alsayed

et al. 2017

IEEE Trans. Intell. Veh.

700

366

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Abstract-In this article, we propose a survey of the Simultaneous Localization And Mapping field when considering the recent evolution of autonomous driving. The growing interest regarding self-driving cars has given new directions to localization and mapping techniques. In this survey, we give an overview of the different branches of SLAM before going into the details of specific trends that are of interest when considered with autonomous applications in mind. We first present the limits of classical approaches for autonomous driving and discuss the criteria that are essential for this kind of application. We then review the methods where the identified challenges are tackled. We mostly focus on approaches building and reusing long-term maps in various conditions (weather, season, etc.). We also go through the emerging domain of multi-vehicle SLAM and its link with self-driving cars. We survey the different paradigms of that field (centralized and distributed) and the existing solutions. Finally, we conclude by giving an overview of the various largescale experiments that have been carried out until now and discuss the remaining challenges and future orientations.

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Section: Relevant Surveys and Existing Data Setsmentioning

confidence: 99%

“…4) Discussion: We have focused on how loop closure is applied in SLAM algorithms. The recent years have seen a clear focus on how to deal with seasonal or weather changes and we refer our readers to this survey [93] for a detailed view of this field, independently of loop closure.…”

Section: A Relocalization and Loop Closurementioning

confidence: 99%

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Bresson

Alsayed

et al. 2017

IEEE Trans. Intell. Veh.

700

366

View full text Add to dashboard Cite

show abstract

“…The term loop closure refers to positively identifying a geometric relation to previous objects or pose positions seen by the navigating platform, and has been the subject of many investigations as summarized by Lowry et al [140]. A loop closure could be as simple as a user input statement, confirming that the navigating platform has been returned exactly to a previous position, using some accurate mechanical alignment technique.…”

Section: Loop Closures (Vital Data Association)mentioning

confidence: 99%

Multi-modal and inertial sensor solutions for navigation-type factor graphs

Fourie¹

2017

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This thesis presents a sum-product inference algorithm for platform navigation called Multi-modal iSAM (incremental smoothing and mapping). Common Gaussianonly likelihoods are restrictive and require a complex front-end processes to deal with non-Gaussian measurements. Instead, our approach allows the front-end to defer ambiguities with non-Gaussian measurement models. We retain the acyclic Bayes tree (and incremental update strategy) from the predecessor iSAM2 maxproduct algorithm [Kaess et al., IJRR 2012]. The approach propagates continuous beliefs on the Bayes (Junction) tree, which is an efficient symbolic refactorization of the nonparametric factor graph, and asymptotically approximates the underlying Chapman-Kolmogorov equations. Our method tracks dominant modes in the marginal posteriors of all variables with minimal approximation error, while suppressing almost all low likelihood modes (in a non-permanent manner). Keeping with existing inertial navigation, we present a novel, continuous-time, retroactively calibrating inertial odometry residual function, using preintegration to seamlessly incorporate pure inertial sensor measurements into a factor graph. We centralize around a factor graph (with starved graph databases) to separate elements of the navigation into an ecosystem of processes. Practical examples are included, such as how to infer multi-modal marginal posterior belief estimates for ambiguous loop closures; raw beam-formed acoustic measurements; or conventional parametric likelihoods, and others.

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“…Most of the aforementioned approaches were aimed at place recognition [7] and metric localization [9]. Unlike these works, we focus on the image processing aspect of long-term navigation in the context of teach-andrepeat systems [10], where a key issue is robust estimation of the robot heading [11,12].…”

Section: Visual Navigation In Changing Environmentsmentioning

confidence: 99%

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

Krajník

Cristóforis

Kusumam

et al. 2017

Robotics and Autonomous Systems

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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.

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Visual Place Recognition: A Survey

Cited by 965 publications

References 199 publications

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Multi-modal and inertial sensor solutions for navigation-type factor graphs

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

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