GROUNDED: The Localizing Ground Penetrating Radar Evaluation Dataset

Ort, Teddy; Gilitschenski, Igor; Rus, Daniela

doi:10.15607/rss.2021.xvii.080

Cited by 5 publications

(2 citation statements)

References 33 publications

(47 reference statements)

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“…From the information entropy, this dataset contains a high amount of information. The data includes left, centre, and right images [38]. The complete images obtained by concatenating the left and right are used as the map data for this dataset, while the centre is used as real-time images for testing, as shown in Figure 14.…”

Section: The Same Weather Of Asphalt Pavementmentioning

confidence: 99%

TSVR-Net: An End-to-End Ground-Penetrating Radar Images Registration and Location Network

Shen

Zhang

et al. 2023

Remote Sensing

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Stable and reliable autonomous localization technology is fundamental for realizing autonomous driving. Localization systems based on global positioning system (GPS), cameras, LIDAR, etc., can be affected by building occlusion or drastic changes in the environment. These effects can degrade the localization accuracy and even cause the problem of localization failure. Localizing ground-penetrating radar (LGPR) as a new type of localization can rely only on robust subsurface information for autonomous localization. LGPR is mostly a 2D-2D registration process. This paper describes the LGPR as a slice-to-volume registration (SVR) problem and proposes an end-to-end TSVR-Net-based regression localization method. Firstly, the information of different dimensions in 3D data is used to ensure the high discriminative power of the data. Then the attention module is added to the design to make the network pay attention to important information and high discriminative regions while balancing the information weights of different dimensions. Eventually, it can directly regress to predict the current data location on the map. We designed several sets of experiments to verify the method’s effectiveness by a step-by-step analysis. The superiority of the proposed method over the current state-of-the-art LGPR method is also verified on five datasets. The experimental results show that both the deep learning method and the increase in dimensional information can improve the stability of the localization system. The proposed method exhibits excellent localization accuracy and better stability, providing a new concept to realize the stable and reliable real-time localization of ground-penetrating radar images.

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Section: The Same Weather Of Asphalt Pavementmentioning

confidence: 99%

TSVR-Net: An End-to-End Ground-Penetrating Radar Images Registration and Location Network

Shen

Zhang

et al. 2023

Remote Sensing

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“…Additionally, the dataset was released with a focus on localisation and thus does not include 2D/3D object or segmentation annotations. The recent GROUNDED dataset [25] is for localization in varying weather conditions, with a focus on ground penetrating radar. The Canadian Adverse Driving Conditions (CADC) dataset [26] includes 7K frames with 3D object labels in snow conditions with surround vision, LiDAR data, and ground truth motion.…”

Section: Related Workmentioning

confidence: 99%

Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions

Diaz-Ruiz¹,

Xia²,

You³

et al. 2022

Preprint

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Advances in perception for self-driving cars have accelerated in recent years due to the availability of large-scale datasets, typically collected at specific locations and under nice weather conditions. Yet, to achieve the high safety requirement, these perceptual systems must operate robustly under a wide variety of weather conditions including snow and rain. In this paper, we present a new dataset to enable robust autonomous driving via a novel data collection process -data is repeatedly recorded along a 15 km route under diverse scene (urban, highway, rural, campus), weather (snow, rain, sun), time (day/night), and traffic conditions (pedestrians, cyclists and cars). The dataset includes images and point clouds from cameras and LiDAR sensors, along with high-precision GPS/INS to establish correspondence across routes. The dataset includes road and object annotations using amodal masks to capture partial occlusions and 3D bounding boxes. We demonstrate the uniqueness of this dataset by analyzing the performance of baselines in amodal segmentation of road and objects, depth estimation, and 3D object detection. The repeated routes opens new research directions in object discovery, continual learning, and anomaly detection. Link to Ithaca365: https://ithaca365.mae.cornell.edu/

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Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions

Diaz-Ruiz

Xia

You

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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GROUNDED: The Localizing Ground Penetrating Radar Evaluation Dataset

Cited by 5 publications

References 33 publications

TSVR-Net: An End-to-End Ground-Penetrating Radar Images Registration and Location Network

TSVR-Net: An End-to-End Ground-Penetrating Radar Images Registration and Location Network

Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions

Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions

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