DIDLM: A SLAM Dataset for Difficult Scenarios Featuring Infrared, Depth Cameras, LIDAR, 4D Radar, and Others under Adverse Weather, Low Light Conditions, and Rough Roads

• URL: http://arxiv.org/abs/2404.09622v2
• Date: Tue, 14 Jan 2025 09:22:35 GMT
• Title: DIDLM: A SLAM Dataset for Difficult Scenarios Featuring Infrared, Depth Cameras, LIDAR, 4D Radar, and Others under Adverse Weather, Low Light Conditions, and Rough Roads
• Authors: Weisheng Gong, Kaijie Su, Qingyong Li, Chen He, Tong Wu, Z. Jane Wang,
• Abstract summary: We introduce a multi-sensor dataset covering challenging scenarios such as snowy weather, rainy weather, nighttime conditions, speed bumps, and rough terrains. The dataset includes rarely utilized sensors for extreme conditions, such as 4D millimeter-wave radar, infrared cameras, and depth cameras, alongside 3D LiDAR, RGB cameras, GPS, and IMU. It supports both autonomous driving and ground robot applications and provides reliable GPS/INS ground truth data, covering structured and semi-structured terrains.
• Score: 20.600516423425688
• License:
• Abstract: Adverse weather conditions, low-light environments, and bumpy road surfaces pose significant challenges to SLAM in robotic navigation and autonomous driving. Existing datasets in this field predominantly rely on single sensors or combinations of LiDAR, cameras, and IMUs. However, 4D millimeter-wave radar demonstrates robustness in adverse weather, infrared cameras excel in capturing details under low-light conditions, and depth images provide richer spatial information. Multi-sensor fusion methods also show potential for better adaptation to bumpy roads. Despite some SLAM studies incorporating these sensors and conditions, there remains a lack of comprehensive datasets addressing low-light environments and bumpy road conditions, or featuring a sufficiently diverse range of sensor data. In this study, we introduce a multi-sensor dataset covering challenging scenarios such as snowy weather, rainy weather, nighttime conditions, speed bumps, and rough terrains. The dataset includes rarely utilized sensors for extreme conditions, such as 4D millimeter-wave radar, infrared cameras, and depth cameras, alongside 3D LiDAR, RGB cameras, GPS, and IMU. It supports both autonomous driving and ground robot applications and provides reliable GPS/INS ground truth data, covering structured and semi-structured terrains. We evaluated various SLAM algorithms using this dataset, including RGB images, infrared images, depth images, LiDAR, and 4D millimeter-wave radar. The dataset spans a total of 18.5 km, 69 minutes, and approximately 660 GB, offering a valuable resource for advancing SLAM research under complex and extreme conditions. Our dataset is available at https://github.com/GongWeiSheng/DIDLM.

Related papers

• RobuRCDet: Enhancing Robustness of Radar-Camera Fusion in Bird's Eye View for 3D Object Detection [68.99784784185019]
  Poor lighting or adverse weather conditions degrade camera performance. Radar suffers from noise and positional ambiguity. We propose RobuRCDet, a robust object detection model in BEV.
  arXiv  Detail & Related papers  (2025-02-18T17:17:38Z)
• Radar Fields: Frequency-Space Neural Scene Representations for FMCW Radar [62.51065633674272]
  We introduce Radar Fields - a neural scene reconstruction method designed for active radar imagers. Our approach unites an explicit, physics-informed sensor model with an implicit neural geometry and reflectance model to directly synthesize raw radar measurements. We validate the effectiveness of the method across diverse outdoor scenarios, including urban scenes with dense vehicles and infrastructure.
  arXiv  Detail & Related papers  (2024-05-07T20:44:48Z)
• NTU4DRadLM: 4D Radar-centric Multi-Modal Dataset for Localization and Mapping [32.0536548410301]
  SLAM based on 4D Radar, thermal camera and IMU can work robustly. The main characteristics are: 1) It is the only dataset that simultaneously includes all 6 sensors: 4D radar, thermal camera, IMU, 3D LiDAR, visual camera and RTK GPS.
  arXiv  Detail & Related papers  (2023-09-02T15:12:20Z)
• ThermRad: A Multi-modal Dataset for Robust 3D Object Detection under Challenging Conditions [15.925365473140479]
  We present a new multi-modal dataset called ThermRad, which includes a 3D LiDAR, a 4D radar, an RGB camera and a thermal camera. We propose a new multi-modal fusion method called RTDF-RCNN, which leverages the complementary strengths of 4D radars and thermal cameras to boost object detection performance. Our method achieves significant enhancements in detecting cars, pedestrians, and cyclists, with improvements of over 7.98%, 24.27%, and 27.15%, respectively.
  arXiv  Detail & Related papers  (2023-08-20T04:34:30Z)
• DensePose From WiFi [86.61881052177228]
  We develop a deep neural network that maps the phase and amplitude of WiFi signals to UV coordinates within 24 human regions. Our model can estimate the dense pose of multiple subjects, with comparable performance to image-based approaches.
  arXiv  Detail & Related papers  (2022-12-31T16:48:43Z)
• MIPI 2022 Challenge on RGBW Sensor Fusion: Dataset and Report [90.34148262169595]
  This paper introduces the first MIPI challenge, including five tracks focusing on novel image sensors and imaging algorithms. The participants were provided with a new dataset including 70 (training) and 15 (validation) scenes of high-quality RGBW and Bayer pairs. All the data were captured using an RGBW sensor in both outdoor and indoor conditions.
  arXiv  Detail & Related papers  (2022-09-15T05:56:53Z)
• K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditions [9.705678194028895]
  KAIST-Radar is a novel large-scale object detection dataset and benchmark. It contains 35K frames of 4D Radar tensor (4DRT) data with power measurements along the Doppler, range, azimuth, and elevation dimensions. We provide auxiliary measurements from carefully calibrated high-resolution Lidars, surround stereo cameras, and RTK-GPS.
  arXiv  Detail & Related papers  (2022-06-16T13:39:21Z)
• All-Weather Object Recognition Using Radar and Infrared Sensing [1.7513645771137178]
  This thesis explores new sensing developments based on long wave polarised infrared (IR) imagery and imaging radar to recognise objects. First, we developed a methodology based on Stokes parameters using polarised infrared data to recognise vehicles using deep neural networks. Second, we explored the potential of using only the power spectrum captured by low-THz radar sensors to perform object recognition in a controlled scenario. Last, we created a new large-scale dataset in the "wild" with many different weather scenarios showing radar robustness to detect vehicles in adverse weather.
  arXiv  Detail & Related papers  (2020-10-30T14:16:39Z)
• 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)
• Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning [59.19469551774703]
  Drone-based vehicle detection aims at finding the vehicle locations and categories in an aerial image. We construct a large-scale drone-based RGB-Infrared vehicle detection dataset, termed DroneVehicle. Our DroneVehicle collects 28, 439 RGB-Infrared image pairs, covering urban roads, residential areas, parking lots, and other scenarios from day to night.
  arXiv  Detail & Related papers  (2020-03-05T05:29:44Z)

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.