Towards Persistent Localization and Mapping with a Continuous Appearance-based Topology

Maddern, William; Milford, Michael; Wyeth, Gordon

doi:10.15607/rss.2012.viii.036

Cited by 14 publications

(15 citation statements)

References 32 publications

(46 reference statements)

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“…In the context of VPR, FAB-MAP [14] formulates topological mapping and localization as a Bayesian state estimation problem and uses handcrafted visual features to represent a place. This base model has been extended to include odometry and image sequences [18], [37]. In comparison, our approach does not perform mapping, however unlike [14], [18], [37], it can successfully localize in a large-scale map across severe appearance change when used in conjunction with a condition invariant VPR method.…”

Section: B Bayesian State Estimation-based Localizationmentioning

confidence: 99%

Probabilistic Visual Place Recognition for Hierarchical Localization

Mei

Snderhauf

Milford

2021

IEEE Robot. Autom. Lett.

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Visual localization techniques often comprise a hierarchical localization pipeline, with a visual place recognition module used as a coarse localizer to initialize a pose refinement stage. While improving the pose refinement step has been the focus of much recent research, most work on the coarse localization stage has focused on improvements like increased invariance to appearance change, without improving what can be loose error tolerances. In this letter, we propose two methods which adapt image retrieval techniques used for visual place recognition to the Bayesian state estimation formulation for localization. We demonstrate significant improvements to the localization accuracy of the coarse localization stage using our methods, whilst retaining state-of-the-art performance under severe appearance change. Using extensive experimentation on the Oxford RobotCar dataset, results show that our approach outperforms comparable state-of-the-art methods in terms of precision-recall performance for localizing image sequences. In addition, our proposed methods provides the flexibility to contextually scale localization latency in order to achieve these improvements. The improved initial localization estimate opens up the possibility of both improved overall localization performance and modified pose refinement techniques that leverage this improved spatial prior.

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Section: B Bayesian State Estimation-based Localizationmentioning

confidence: 99%

Probabilistic Visual Place Recognition for Hierarchical Localization

Mei

Snderhauf

Milford

2021

IEEE Robot. Autom. Lett.

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“…Maddern et al. () use the information content of a view relative to its neighbors to select views for deletion and replace them with relative constraints. In all of these approaches, the primary goal is to produce a consistent map—the question of what is best for localization is not addressed.…”

Section: Related Workmentioning

confidence: 99%

Summary Maps for Lifelong Visual Localization

et al. 2015

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Robots that use vision for localization need to handle environments that are subject to seasonal and structural change, and operate under changing lighting and weather conditions. We present a framework for lifelong localization and mapping designed to provide robust and metrically accurate online localization in these kinds of changing environments. Our system iterates between offline map building, map summary, and online localization. The offline mapping fuses data from multiple visually varied datasets, thus dealing with changing environments by incorporating new information. Before passing these data to the online localization system, the map is summarized, selecting only the landmarks that are deemed useful for localization. This Summary Map enables online localization that is accurate and robust to the variation of visual information in natural environments while still being computationally efficient. We present a number of summary policies for selecting useful features for localization from the multisession map, and we explore the tradeoff between localization performance and computational complexity. The system is evaluated on 77 recordings, with a total length of 30 kilometers, collected outdoors over 16 months. These datasets cover all seasons, various times of day, and changing weather such as sunshine, rain, fog, and snow. We show that it is possible to build consistent maps that span data collected over an entire year, and cover day‐to‐night transitions. Simple statistics computed on landmark observations are enough to produce a Summary Map that enables robust and accurate localization over a wide range of seasonal, lighting, and weather conditions.

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“…For a real-world deployment where certain streets might have been captured hundreds of times, we thus need to solve a non-trivial subselection and compression problem: build and maintain a model of the environment which is constant in memory size, yet capable of incorporating newly collected data over time. Early work in the robotics community discarded entire image captures from the map (Maddern et al, 2012a,b) or formed full image descriptors (Naseer et al, 2014) used to identify novel views of the scene. Discarding entire images, however, ignores the fine-grained information about stable environment features that are critical for robust localization.…”

Section: The Need For Model Compressionmentioning

confidence: 99%

Large-scale, real-time visual–inertial localization revisited

Lynen

Zeisl

Aiger

et al. 2020

The International Journal of Robotics Research

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The overarching goals in image-based localization are scale, robustness, and speed. In recent years, approaches based on local features and sparse 3D point-cloud models have both dominated the benchmarks and seen successful real-world deployment. They enable applications ranging from robot navigation, autonomous driving, virtual and augmented reality to device geo-localization. Recently, end-to-end learned localization approaches have been proposed which show promising results on small-scale datasets. However, the positioning accuracy, scalability, latency, and compute and storage requirements of these approaches remain open challenges. We aim to deploy localization at a global scale where one thus relies on methods using local features and sparse 3D models. Our approach spans from offline model building to real-time client-side pose fusion. The system compresses the appearance and geometry of the scene for efficient model storage and lookup leading to scalability beyond what has been demonstrated previously. It allows for low-latency localization queries and efficient fusion to be run in real-time on mobile platforms by combining server-side localization with real-time visual–inertial-based camera pose tracking. In order to further improve efficiency, we leverage a combination of priors, nearest-neighbor search, geometric match culling, and a cascaded pose candidate refinement step. This combination outperforms previous approaches when working with large-scale models and allows deployment at unprecedented scale. We demonstrate the effectiveness of our approach on a proof-of-concept system localizing 2.5 million images against models from four cities in different regions of the world achieving query latencies in the 200 ms range.

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Towards Persistent Localization and Mapping with a Continuous Appearance-based Topology

Cited by 14 publications

References 32 publications

Probabilistic Visual Place Recognition for Hierarchical Localization

Probabilistic Visual Place Recognition for Hierarchical Localization

Summary Maps for Lifelong Visual Localization

Large-scale, real-time visual–inertial localization revisited

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