Leveraging experience for large-scale LIDAR localisation in changing cities

Maddern, Will; Pascoe, Geoffrey; Newman, Paul

doi:10.1109/icra.2015.7139414

Cited by 48 publications

(24 citation statements)

References 25 publications

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“…It is expected that the retail price of these LiDAR scanners will fall quickly because there are more than 40 manufacturers competing in this rising field as we are writing this article. Rather than using multi-layer LiDARs, works [13], [14], [15], [16], [17], [18], [19] attempt to accomplish the similar task with 2D or low-end LiDARs.…”

Section: Related Workmentioning

confidence: 99%

Robust and Precise Vehicle Localization Based on Multi-Sensor Fusion in Diverse City Scenes

Wan¹,

Yang²,

Cai³

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

268

111

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We present a robust and precise localization system that achieves centimeter-level localization accuracy in disparate city scenes. Our system adaptively uses information from complementary sensors such as GNSS, LiDAR, and IMU to achieve high localization accuracy and resilience in challenging scenes, such as urban downtown, highways, and tunnels. Rather than relying only on LiDAR intensity or 3D geometry, we make innovative use of LiDAR intensity and altitude cues to significantly improve localization system accuracy and robustness. Our GNSS RTK module utilizes the help of the multi-sensor fusion framework and achieves a better ambiguity resolution success rate. An error-state Kalman filter is applied to fuse the localization measurements from different sources with novel uncertainty estimation. We validate, in detail, the effectiveness of our approaches, achieving 5-10cm RMS accuracy and outperforming previous state-of-the-art systems. Importantly, our system, while deployed in a large autonomous driving fleet, made our vehicles fully autonomous in crowded city streets despite road construction that occurred from time to time. A dataset including more than 60 km real traffic driving in various urban roads is used to comprehensively test our system.

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Section: Related Workmentioning

confidence: 99%

Robust and Precise Vehicle Localization Based on Multi-Sensor Fusion in Diverse City Scenes

Wan¹,

Yang²,

Cai³

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

268

111

View full text Add to dashboard Cite

show abstract

“…Pioneered in [1], [2], SLAM remains most developed in mobile robotics, where LIDAR and large baseline stereo rigs measure absolute depth and allow mapping of trajectories of kilometres in length [3], [4], [5]. Deploying SLAM for augmented reality on mobiles and wearables imposes much tighter size and power budgets, making the pursuit of single-camera SLAM an imperative [6], [7], [8], [9].…”

Section: Introductionmentioning

confidence: 98%

Object-aware bundle adjustment for correcting monocular scale drift

Frost

Kähler

Murray

2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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Without knowledge of the absolute baseline between images, the scale of a map from single-camera simultaneous localization and mapping system is subject to calamitous drift over time. We describe a monocular approach that in addition to point measurements also considers object detections to resolve this scale ambiguity and drift. By placing a prior on the size of the objects, the scale estimation can be seamlessly integrated into a bundle adjustment. When object observations are available, the local scale of the map is then determined jointly with the camera pose in local adjustments. Unlike many previous visual odometry methods, our approach does not impose restrictions such as approximately constant camera height or planar roadways, and is therefore applicable to a much wider range of applications. We evaluate our approach on the KITTI dataset and show that it reduces scale drift over long-range outdoor sequences with a total length of 40 km. Qualitative evaluation is also performed on video footage from a hand-held camera.

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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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Leveraging experience for large-scale LIDAR localisation in changing cities

Cited by 48 publications

References 25 publications

Robust and Precise Vehicle Localization Based on Multi-Sensor Fusion in Diverse City Scenes

Robust and Precise Vehicle Localization Based on Multi-Sensor Fusion in Diverse City Scenes

Object-aware bundle adjustment for correcting monocular scale drift

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

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