The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
The Multivehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception
Open accessAbstract
Event-based cameras are a new passive sensing modality with a number of benefits over traditional cameras, including extremely low latency, asynchronous data acquisition, high dynamic range, and very low power consumption. There has been a lot of recent interest and development in applying algorithms to use the events to perform a variety of three-dimensional perception tasks, such as feature tracking, visual odometry, and stereo depth estimation. However, there currently lacks the wealth of labeled data that exists for traditional cameras to be used for both testing and development. In this letter, we present a large dataset with a synchronized stereo pair event based camera system, carried on a handheld rig, flown by a hexacopter, driven on top of a car, and mounted on a motorcycle, in a variety of different illumination levels and environments. From each camera, we provide the event stream, grayscale images, and inertial measurement unit (IMU) readings. In addition, we utilize a combination of IMU, a rigidly mounted lidar system, indoor and outdoor motion capture, and GPS to provide accurate pose and depth images for each camera at up to 100 Hz. For comparison, we also provide synchronized grayscale images and IMU readings from a frame-based stereo camera system.
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Cites
LOAM: Lidar Odometry and Mapping in Real-time
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Real-time loop closure in 2D LIDAR SLAM
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AprilTag: A robust and flexible visual fiducial system
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A 240 × 180 130 dB 3 µs Latency Global Shutter Spatiotemporal Vision Sensor
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Unified temporal and spatial calibration for multi-sensor systems
Furgale, P., Rehder, J., & Siegwart, R. (2013) · 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems · doi:10.1109/iros.2013.6696514 · Cited by 807
Hand-Eye Calibration Using Dual Quaternions
Daniilidis, K. (1999) · The International Journal of Robotics Research · doi:10.1177/02783649922066213 · Cited by 729
Self-citationAutomatic camera and range sensor calibration using a single shot
Geiger, A., Moosmann, F., Car, O., & Schuster, B. (2012) · 2012 IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2012.6224570 · Cited by 647
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Real-Time 3D Reconstruction and 6-DoF Tracking with an Event Camera
Kim, H., Leutenegger, S., & Davison, A. J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46466-4_21 · Cited by 438
EVO: A Geometric Approach to Event-Based 6-DOF Parallel Tracking and Mapping in Real Time
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Cited by
Gallego, G., Delbruck, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., Leutenegger, S., Davison, A. J., Conradt, J., Daniilidis, K., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3008413 · Cited by 2,566
Self-citationEV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras
Zhu, A., Yuan, L., Chaney, K., & Daniilidis, K. (2018) · Robotics: Science and Systems XIV · doi:10.15607/rss.2018.xiv.062 · Cited by 470
Self-citationEvents-To-Video: Bringing Modern Computer Vision to Event Cameras
Rebecq, H., Ranftl, R., Koltun, V., & Scaramuzza, D. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00398 · Cited by 420
Spiking Neural Networks Hardware Implementations and Challenges: A Survey
Bouvier, M., Valentian, A., Mesquida, T., Rummens, F., Reyboz, M., Vianello, E., & Beigne, E. (2019) · ACM Journal on Emerging Technologies in Computing Systems · doi:10.1145/3304103 · Cited by 267
Rizzoli, G., Barbato, F., & Zanuttigh, P. (2022) · Technologies · doi:10.3390/technologies10040090 · Cited by 58
Metadata
- Authors
- Zhu, A. Z., Thakur, D., Ozaslan, T., Pfrommer, B., Kumar, V., & Daniilidis, K.
- Venue
- IEEE Robotics and Automation Letters
- Published
- 2018-07
- Volume
- 3
- Issue
- 3
- Pages
- 2032-2039
- ISSN
- 2377-3766 (ISSN-L)
- 2377-3774
- OpenAlex
- W2785582094
- Cited by
- 567
- References
- 35
- FWCI
- 19.19
- Type
- journal article
- Language
- en
- Open access
- green
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- Zhu, A. Z., Thakur, D., Ozaslan, T., Pfrommer, B., Kumar, V., & Daniilidis, K. (2018). The Multivehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception. IEEE Robotics and Automation Letters. https://doi.org/10.1109/lra.2018.2800793
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