SemanticSpray++: A Multimodal Dataset for Autonomous Driving in Wet Surface Conditions

• URL: http://arxiv.org/abs/2406.09945v1
• Date: Fri, 14 Jun 2024 11:46:48 GMT
• Title: SemanticSpray++: A Multimodal Dataset for Autonomous Driving in Wet Surface Conditions
• Authors: Aldi Piroli, Vinzenz Dallabetta, Johannes Kopp, Marc Walessa, Daniel Meissner, Klaus Dietmayer,
• Abstract summary: The SemanticSpray++ dataset provides labels for camera, LiDAR, and radar data of highway-like scenarios in wet surface conditions. By labeling all three sensor modalities, the dataset offers a comprehensive test bed for analyzing the performance of different perception methods.
• Score: 10.306226508237348
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous vehicles rely on camera, LiDAR, and radar sensors to navigate the environment. Adverse weather conditions like snow, rain, and fog are known to be problematic for both camera and LiDAR-based perception systems. Currently, it is difficult to evaluate the performance of these methods due to the lack of publicly available datasets containing multimodal labeled data. To address this limitation, we propose the SemanticSpray++ dataset, which provides labels for camera, LiDAR, and radar data of highway-like scenarios in wet surface conditions. In particular, we provide 2D bounding boxes for the camera image, 3D bounding boxes for the LiDAR point cloud, and semantic labels for the radar targets. By labeling all three sensor modalities, the SemanticSpray++ dataset offers a comprehensive test bed for analyzing the performance of different perception methods when vehicles travel on wet surface conditions. Together with comprehensive label statistics, we also evaluate multiple baseline methods across different tasks and analyze their performances. The dataset will be available at https://semantic-spray-dataset.github.io .

Related papers

• Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Exploration [55.41644538483948]
  This paper introduces a multimodal dataset from the harsh and unstructured underground environment with aerosol particles. It contains synchronized raw data measurements from all onboard sensors in Robot Operating System (ROS) format. The focus of this paper is not only to capture both temporal and spatial data diversities but also to present the impact of harsh conditions on captured data.
  arXiv  Detail & Related papers  (2023-04-27T20:21:18Z)
• Point Cloud Forecasting as a Proxy for 4D Occupancy Forecasting [58.45661235893729]
  One promising self-supervised task is 3D point cloud forecasting from unannotated LiDAR sequences. We show that this task requires algorithms to implicitly capture (1) sensor extrinsics (i.e., the egomotion of the autonomous vehicle), (2) sensor intrinsics (i.e., the sampling pattern specific to the particular LiDAR sensor), and (3) the shape and motion of other objects in the scene. We render point cloud data from 4D occupancy predictions given sensor extrinsics and intrinsics, allowing one to train and test occupancy algorithms with unannotated LiDAR sequences.
  arXiv  Detail & Related papers  (2023-02-25T18:12:37Z)
• SUPS: A Simulated Underground Parking Scenario Dataset for Autonomous Driving [41.221988979184665]
  SUPS is a simulated dataset for underground automatic parking. It supports multiple tasks with multiple sensors and multiple semantic labels aligned with successive images. We also evaluate the state-of-the-art SLAM algorithms and perception models on our dataset.
  arXiv  Detail & Related papers  (2023-02-25T02:59:12Z)
• LiDAR-CS Dataset: LiDAR Point Cloud Dataset with Cross-Sensors for 3D Object Detection [36.77084564823707]
  deep learning methods heavily rely on annotated data and often face domain generalization issues. LiDAR-CS dataset is the first dataset that addresses the sensor-related gaps in the domain of 3D object detection in real traffic.
  arXiv  Detail & Related papers  (2023-01-29T19:10:35Z)
• Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting [64.7364925689825]
  Argoverse 2 (AV2) is a collection of three datasets for perception and forecasting research in the self-driving domain. The Lidar dataset contains 20,000 sequences of unlabeled lidar point clouds and map-aligned pose. The Motion Forecasting dataset contains 250,000 scenarios mined for interesting and challenging interactions between the autonomous vehicle and other actors in each local scene.
  arXiv  Detail & Related papers  (2023-01-02T00:36:22Z)
• Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions [0.0]
  We present a new dataset to enable robust autonomous driving via a novel data collection process. The dataset includes images and point clouds from cameras and LiDAR sensors, along with high-precision GPS/INS. We demonstrate the uniqueness of this dataset by analyzing the performance of baselines in amodal segmentation of road and objects.
  arXiv  Detail & Related papers  (2022-08-01T22:55:32Z)
• Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection [58.81316192862618]
  Two critical sensors for 3D perception in autonomous driving are the camera and the LiDAR. fusing these two modalities can significantly boost the performance of 3D perception models. We benchmark the state-of-the-art fusion methods for the first time.
  arXiv  Detail & Related papers  (2022-05-30T09:35:37Z)
• Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather [92.84066576636914]
  This work addresses the challenging task of LiDAR-based 3D object detection in foggy weather. We tackle this problem by simulating physically accurate fog into clear-weather scenes. We are the first to provide strong 3D object detection baselines on the Seeing Through Fog dataset.
  arXiv  Detail & Related papers  (2021-08-11T14:37:54Z)
• RADIATE: A Radar Dataset for Automotive Perception in Bad Weather [13.084162751635239]
  RADIATE includes 3 hours of annotated radar images with more than 200K labelled road actors in total. It covers 8 different categories of actors in a variety of weather conditions. RADIATE also has stereo images, 32-channel LiDAR and GPS data, directed at other applications.
  arXiv  Detail & Related papers  (2020-10-18T19:33:27Z)
• LIBRE: The Multiple 3D LiDAR Dataset [54.25307983677663]
  We present LIBRE: LiDAR Benchmarking and Reference, a first-of-its-kind dataset featuring 10 different LiDAR sensors. LIBRE will contribute to the research community to provide a means for a fair comparison of currently available LiDARs. It will also facilitate the improvement of existing self-driving vehicles and robotics-related software.
  arXiv  Detail & Related papers  (2020-03-13T06:17:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.