The Málaga urban dataset: High-rate stereo and LiDAR in a realistic urban scenario
Abstract
This paper introduces a dataset gathered entirely in urban scenarios with a car equipped with one stereo camera and five laser scanners, among other sensors. One distinctive feature of the present dataset is the existence of high-resolution stereo images grabbed at a high rate (20 fps) during a 36.8 km trajectory, which allows the benchmarking of a variety of computer vision techniques. We describe the sensors employed and highlight some applications which could be benchmarked using the present work. Both plain text and binary files are provided, as well as open-source tools for working with the binary versions. The dataset is available for download at http://www.mrpt.org/MalagaUrbanDataset .
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Some works extended the nuScenes dataset to introduce new annotations for natural language object referral and high-level scene understanding.
“Some works extended our dataset to introduce new annotations for natural language object referral [22] and highlevel scene understanding [74].”
From nuScenes: A Multimodal Dataset for Autonomous Driving (2020) · cites this paper as [74]
The Malaga Stereo and Laser Urban dataset has no task.
“no”
From Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2021) · cites this paper as [76]
The Malaga Stereo and Laser Urban dataset uses visual (stereo) camera, 5×2D LiDAR (yielding 3D information), GNSS and inertial sensors.
“Visual (Stereo) camera, 5 × 2D LiDAR (yielding 3D information), GNSS and inertial sensors”
From Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2021) · cites this paper as [76]
The Malaga Stereo and Laser Urban dataset was collected in 2014.
“2014”
From Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2021) · cites this paper as [76]
The Malaga Stereo and Laser Urban dataset was collected in Malaga.
“M´ alaga”
From Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2021) · cites this paper as [76]
The Malaga Stereo and Laser Urban dataset has n.a. objects.
“n.a.”
From Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2021) · cites this paper as [76]
The Malaga Stereo and Laser Urban dataset contains 113,082 frames, 5,654.6s (camera); >220,000 frames, 5,000s (LiDARs).
“113 , 082frames, 5 , 654 . 6s (camera); > 220 , 000frames, 5 , 000s (LiDARs)”
From Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2021) · cites this paper as [76]
VIOLearner reaches state-of-the-art performance on KITTI among learning-based approaches.
“VIOLearner achieves SoA performance on KITTI [1] for learning-based approaches.”
From Unsupervised Deep Visual-Inertial Odometry with Online Error Correction for RGB-D Imagery (2020) · cites this paper as [1]
The KITTI dataset contains RGB and depth data over 39.2 km and was ground truthed within 10 cm using an OXTS RT 3003 GPS solution.
“The KITTI dataset [1] included RGB and depth data captured over 39.2 km and was ground truthed with accuracies within 10cm via an OXTS RT 3003 GPS solution.”
From Unsupervised Deep Visual-Inertial Odometry with Online Error Correction for RGB-D Imagery (2020) · cites this paper as [1]
KITTI used only software synchronization between sensors, which causes problems for some VIO approaches because imagery and IMU data are not accurately time-synchronized.
“However, KITTI only used software synchronization between sensors which causes issues for some VIO approaches due to the inaccurate time synchronization between imagery and IMU data.”
From Unsupervised Deep Visual-Inertial Odometry with Online Error Correction for RGB-D Imagery (2020) · cites this paper as [1]
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nuScenes: A Multimodal Dataset for Autonomous Driving
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“Other notable multimodal datasets include [15] providing driving behavior labels, [43] providing place categorization labels and [6, 55] providing raw data without semantic labels.”
Background1 year, 1000 km: The Oxford RobotCar dataset
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- Authors
- Blanco-Claraco, J.-L., Moreno-Dueñas, F.-Á., & González-Jiménez, J.
- Affiliations
- Department of Mechanical Engineering, University of Almería, Spain
- Department of System Engineering and Automation, University of Málaga, Spain
- Universidad de Málaga (ES)
- Venue
- The International Journal of Robotics Research
- Publisher
- SAGE Publications
- Published
- 2013-10-28
- Volume
- 33
- Issue
- 2
- Pages
- 207-214
- ISSN
- 0278-3649 (ISSN-L)
- 1741-3176
- OpenAlex
- W2155695215
- Cited by
- 262
- References
- 12
- Citations by year
- 2026: 13 · 2025: 18 · 2024: 19 · 2023: 17 · 2022: 28 · 2021: 33 · 2020: 37 · 2019: 30 · 2018: 22 · 2017: 20 · 2016: 10 · 2015: 12 · 2014: 3
- FWCI
- 9.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The International Journal of Robotics Research (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- stereo vision, LiDAR, mobile robot sensing, multi-sensor fusion, open source tools
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