1 year, 1000 km: The Oxford RobotCar dataset

Maddern, Will; Pascoe, Geoffrey; Linegar, Chris; Newman, Paul

doi:10.1177/0278364916679498

Cited by 1,319 publications

(1,116 citation statements)

References 27 publications

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“…For example, the images are usually taken from arbitrary angles and are not annotated with navigation information or paired with other sensor data. In response to this need, the robotics community has released a number of datasets focusing on active areas of research such as autonomous driving (Cordts et al, ; Geiger, Lenz, Stiller, & Urtasun, ; X. Huang et al, ; Maddern, Pascoe, Linegar, & Newman, ; Pandey, McBride, & Eustice, ; Yu et al, ). By nature, these data are domain specific and do not necessarily translate to agricultural applications.…”

Section: Introductionmentioning

confidence: 99%

A high‐resolution, multimodal data set for agricultural robotics: A Ladybird's‐eye view of Brassica

Bender

Whelan

Sukkarieh

2019

Journal of Field Robotics

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This article presents an agricultural data set collected by Ladybird, an autonomous field robot designed at the Australian Centre for Field Robotics. The data set contains weekly scans of cauliflower and broccoli (Brassica oleracea) covering a 10 week growth cycle from transplant to harvest. The data set includes ground truth; physical characteristics of the crop; environmental data collected by a weather station and a soil sensor network; and scans of the crop performed by Ladybird, which include stereo color, thermal and hyperspectral imagery. The layout of the farm and data collection methodology are described. A description of Ladybird's capabilities and sensors are provided. Benchmark results are provided to illustrate the contents of the data set and how it can be processed. The hyperspectral data are compiled into hypercubes and the pixels are classified into crop, weed, or soil. An object detector is applied to the color imagery to locate the crop. Our intention in releasing the data set is to facilitate robotics and machine learning research activity in agriculture. The data set can be downloaded from https://doi.org/10.25910/5c941d0c8bccb.

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Section: Introductionmentioning

confidence: 99%

A high‐resolution, multimodal data set for agricultural robotics: A Ladybird's‐eye view of Brassica

Bender

Whelan

Sukkarieh

2019

Journal of Field Robotics

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“…Our approach differentiates itself from existing solutions on various fronts as shown in Table I. We evaluate the performance of our proposed approach on various publicly-available datasets including the KITTI dataset [21], the Multi-FOV synthetic dataset [27] (pinhole, fisheye, and catadioptric lenses), an omnidirectionalcamera dataset [28], and on the Oxford Robotcar 1000km Dataset [29].…”

Section: Methodsmentioning

confidence: 99%

“…In our experiments, we found that while our proposed solution was sufficiently powerful to model different camera optics, it was significantly better at modeling pinhole lenses as compared to fisheye and Sensor fusion with learned ego-motion: On fusing our proposed VO method with intermittent GPS updates (every 150 frames, black circles), the pose-graph optimized ego-motion solution (in green) achieves sufficiently high accuracy relative to ground truth. We test on a variety of publicly-available datasets including (a) Multi-FOV synthetic dataset [27] (pinhole shown above), (b) an omnidirectional-camera dataset [28], (c) Oxford Robotcar 1000km Dataset [29] (2015-11-13-10-28-08) (d-h) KITTI dataset [21]. Weak supervision such as GPS measurements can be especially advantageous in recovering improved estimates for localization, while simultaneously minimizing uncertainties associated with pure VO-based approaches.…”

Section: B Varied Camera Opticsmentioning

confidence: 99%

“…We experiment and validate this concept through a concrete example using the 1000km Oxford Robot Car dataset [29]. We train the task of visual ego-motion on a new camera sensor by leveraging the fused GPS and INS information collected on the robot car as ground truth trajectories (6-DOF), and extracting feature trajectories (via KLT) from image sequences obtained from the new camera sensor.…”

Section: Self-supervised Visual Ego-motion Learning In Robotsmentioning

confidence: 99%

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Towards visual ego-motion learning in robots

Pillai

Leonard

2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-Many model-based Visual Odometry (VO) algorithms have been proposed in the past decade, often restricted to the type of camera optics, or the underlying motion manifold observed. We envision robots to be able to learn and perform these tasks, in a minimally supervised setting, as they gain more experience. To this end, we propose a fully trainable solution to visual ego-motion estimation for varied camera optics. We propose a visual ego-motion learning architecture that maps observed optical flow vectors to an ego-motion density estimate via a Mixture Density Network (MDN). By modeling the architecture as a Conditional Variational Autoencoder (C-VAE), our model is able to provide introspective reasoning and prediction for ego-motion induced scene-flow. Additionally, our proposed model is especially amenable to bootstrapped egomotion learning in robots where the supervision in ego-motion estimation for a particular camera sensor can be obtained from standard navigation-based sensor fusion strategies (GPS/INS and wheel-odometry fusion). Through experiments, we show the utility of our proposed approach in enabling the concept of self-supervised learning for visual ego-motion estimation in autonomous robots.

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“…The Oxford data set (Maddern et al, ): Provided by Oxford University, UK, the data set collection spanned over 1 year, resulting in over 1,000 km of recorded driving with almost 20 million images collected from six cameras mounted to the vehicle, along with LiDAR, GPS, and INS ground truth. Data were collected in all weather conditions, including heavy rain, night, direct sunlight, and snow.…”

Section: Data Sources For Training Autonomous Driving Systemsmentioning

confidence: 99%

A survey of deep learning techniques for autonomous driving

Grigorescu

Trasnea

Cocias

et al. 2019

Journal of Field Robotics

1,127

495

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The last decade witnessed increasingly rapid progress in self‐driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence (AI). The objective of this paper is to survey the current state‐of‐the‐art on deep learning technologies used in autonomous driving. We start by presenting AI‐based self‐driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. These methodologies form a base for the surveyed driving scene perception, path planning, behavior arbitration, and motion control algorithms. We investigate both the modular perception‐planning‐action pipeline, where each module is built using deep learning methods, as well as End2End systems, which directly map sensory information to steering commands. Additionally, we tackle current challenges encountered in designing AI architectures for autonomous driving, such as their safety, training data sources, and computational hardware. The comparison presented in this survey helps gain insight into the strengths and limitations of deep learning and AI approaches for autonomous driving and assist with design choices.

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1 year, 1000 km: The Oxford RobotCar dataset

Cited by 1,319 publications

References 27 publications

A high‐resolution, multimodal data set for agricultural robotics: A Ladybird's‐eye view of Brassica

A high‐resolution, multimodal data set for agricultural robotics: A Ladybird's‐eye view of Brassica

Towards visual ego-motion learning in robots

A survey of deep learning techniques for autonomous driving

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