Lighting‐invariant Visual Teach and Repeat Using Appearance‐based Lidar

McManus, Colin; Furgale, Paul; Stenning, Braden; Barfoot, Timothy D.

doi:10.1002/rob.21444

Cited by 37 publications

(28 citation statements)

References 68 publications

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“…It is possible for the quality of the INS calibration relative to the camera system to degrade, as well as for biases to be introduced by the nature of the DGPS system that is employed within the INS. Similar difficulties using DGPS for ground‐truth assessment have been reported in other papers (Churchill & Newman, ; McManus, Furgale, Stenning, & Barfoot, ). We have omitted several recordings from the evaluation, where clear biases, or missing samples, were observed for the INS.…”

Section: Resultssupporting

confidence: 80%

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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Section: Resultssupporting

confidence: 80%

Summary Maps for Lifelong Visual Localization

et al. 2015

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“…There are a number of ways to address long‐term robustness, one of which is using alternative sensing modalities. For example, lidar has good lighting invariance, and can be used as a standalone sensor (Barfoot et al, ; Maddern, Pascoe, & Newman, ; McManus, Furgale, Stenning, & Barfoot, ), complementary sensor, or to build high‐fidelity maps against which to localize using cameras (Pascoe, Maddern, Stewart, & Newman, ; Wolcott & Eustice, ). This paper focuses on pure vision, motivated by exploring how far we can take a single sensor before worrying about integration in a multisensor system.…”

Section: Introductionmentioning

confidence: 99%

Selective memory: Recalling relevant experience for long‐term visual localization

MacTavish

Paton

Barfoot

2018

Journal of Field Robotics

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Visual navigation is a key enabling technology for autonomous mobile vehicles. The ability to provide large-scale, long-term navigation using low-cost, low-power vision sensors is appealing for industrial applications. A crucial requirement for long-term navigation systems is the ability to localize in environments whose appearance is constantly changing over time-due to lighting, weather, seasons, and physical changes. This paper presents a multiexperience localization (MEL) system that uses a powerful map representation-storing every visual experience in layers-that does not make assumptions about underlying appearance modalities and generators. Our localization system provides real-time performance by selecting online, a subset of experiences against which to localize. We achieve this task through a novel experience-triage algorithm based on collaborative filtering, which selects experiences relevant to the live view, outperforming competing techniques. Based on classical memory-based recommender systems, this technique also enables landmarklevel recommendations, is entirely online, and requires no training data. We demonstrate the capabilities of the MEL system in the context of long-term autonomous path following in unstructured outdoor environments with a challenging 100-day field experiment through day, night, snow, spring, and summer. We furthermore provide offline analysis comparing our system to several state-of-theart alternatives. We show that the combination of the novel methods presented in this paper enable full use of incredibly rich multiexperience maps, opening the door to robust long-term visual localization.

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“…Furgale and Barfoot's system has been extended to other 3D sensors such as lidar [16] and RGB-D cameras, but a monocular implementation has not been forthcoming. While monocular cameras are appealing in terms of size, cost, and simplicity, perhaps the most compelling motivation for using monocular vision for VT&R is the plethora of existing mobile robots that would benefit from it.…”

Section: Introductionmentioning

confidence: 99%

Monocular Visual Teach and Repeat Aided by Local Ground Planarity

Clement¹,

Kelly²,

Barfoot³

2016

Springer Tracts in Advanced Robotics

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Visual Teach and Repeat (VT\&R) allows an autonomous vehicle to repeat a previously traversed route without a global positioning system. Existing implementations of VT\&R typically rely on 3D sensors such as stereo cameras for mapping and localization, but many mobile robots are equipped with only 2D monocular vision for tasks such as teleoperated bomb disposal. While simultaneous localization and mapping (SLAM) algorithms exist that can recover 3D structure and motion from monocular images, the scale ambiguity inherent in these methods complicates the estimation and control of lateral path-tracking error, which is essential for achieving high-accuracy path following. In this paper, we propose a monocular vision pipeline that enables kilometre-scale route repetition with centimetre-level accuracy by approximating the ground surface near the vehicle as planar (with some uncertainty) and recovering absolute scale from the known position and orientation of the camera relative to the vehicle. This system provides added value to many existing robots by allowing for high-accuracy autonomous route repetition with a simple software upgrade and no additional sensors. We validate our system over 4.3 km of autonomous navigation and demonstrate accuracy on par with the conventional stereo pipeline, even in highly non-planar terrain.Comment: In Proceedings of the International Conference on Field and Service Robotics (FSR'15), Toronto, Ontario, Canada, Jun. 24-26 201

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Lighting‐invariant Visual Teach and Repeat Using Appearance‐based Lidar

Cited by 37 publications

References 68 publications

Summary Maps for Lifelong Visual Localization

Summary Maps for Lifelong Visual Localization

Selective memory: Recalling relevant experience for long‐term visual localization

Monocular Visual Teach and Repeat Aided by Local Ground Planarity

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