EVO: A Geometric Approach to Event-Based 6-DOF Parallel Tracking and Mapping in Real Time

By Henri Rebecq, Timo Horstschaefer, Guillermo Gallego, Davide Scaramuzza. IEEE Robotics and Automation Letters, 2017

406 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering, Instrumentation

View Paper: https://www.semanticscholar.org/paper/8651476fca593fc925e93067f6fb61bb4a054e5a

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Citations in the corpus

Event-Based Vision: A Survey (2019, methodology): “Concurrently, the VO methods in [25], [26] extended the image reconstruction technique in [24] to 6-DOF camera motions by using the computed scene depth and poses: [25] used a robust variational regularizer to reduce noise and improve contrast of the reconstructed image, whereas [26] showed image…”
Bringing a Blurry Frame Alive at High Frame-Rate With an Event Camera (2018, background): “Although several works try to explore the advantages of the high temporal resolution provided by event cameras [41, 13, 26, 43, 42, 8, 15, 30], how to make the best use of the event camera has not yet been fully investigated.”
Computing Systems for Autonomous Driving: State of the Art and Challenges (2020, background): “For situations that lack computation resources, semiDense [128], [129] SLAM”
The Multivehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception (2018, background): “The authors in [21] use events to estimate angular velocity of a camera, while [22] and [23] perform visual odometry by building an up to a scale map.”
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016, neutral): “high-speed motion [90] and high-dynamic range [132], [207], where standard cameras fail.”
Events-To-Video: Bringing Modern Computer Vision to Event Cameras (2019, background): “Such specialized algorithms have demonstrated impressive performance in applications ranging from low-level vision tasks, such as visual odometry [18, 39, 53, 38, 41], feature tracking [20, 52, 15] and optical flow [5, 3, 47, 55, 46], to high-level tasks such as object classification [35, 21, 45] an”
Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art (2017, background): “Rebecq et al. (2016) propose an event-based 3D reconstruction algorithm to produce a parallel tracking and mapping pipeline that runs in real-time on the CPU.”
Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High-Speed Scenarios (2017, background): “In fact, this has been shown recently in [8] and [9].”
A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation (2018, background): “That is, consider the problem of event-based multi-view stereo (EMVS) [21, 32] (3D reconstruction) from a set of events E = { e k } N e k = 1 .”
A General Optimization-based Framework for Local Odometry Estimation with Multiple Sensors (2019, methodology): “We have seen many impressive approaches that work with one kind of sensor, such as visual-based methods [1]–[5], LiDAR-based methods [13], RGB-D based methods [14]. and event-based methods [15].”
HATS: Histograms of Averaged Time Surfaces for Robust Event-Based Object Classification (2018, methodology): “Event-based Features and Object Classification Simultaneous Localization and Mapping use-cases [25, 54] drove the majority of prior work on event-based features [11, 43] for stable detection and tracking.”
Reducto: On-Camera Filtering for Resource-Efficient Real-Time Video Analytics (2020, background): “The computer vision (CV) community [20, 24, 44, 46, 59, 60, 66, 68, 82, 83] has discovered a slew of low-level video features that extract frame differences [21], such as Edge and Pixel .”
The event-camera dataset and simulator: Event-based data for pose estimation, visual odometry, and SLAM (2016, background): “…the use of event-based sensors in a variety of tasks, such as SLAM in 2D (Weikersdorfer et al. 2013) and 3D (Kueng et al. 2016; Kim et al. 2016; Rebecq et al. 2016b), optical ﬂow (Cook et al. 2011; Benosman et al. 2014; Bardow et al. 2016), visual odometry (Censi and Scaramuzza 2014), 6-DOF…”
A General Optimization-based Framework for Global Pose Estimation with Multiple Sensors (2019, methodology): “Impressive approaches over the last decades include visual-based methods [1]–[5], LiDAR-based methods [12], RGB-D based methods [13], and event-based methods [14].”
Event-Based Vision Meets Deep Learning on Steering Prediction for Self-Driving Cars (2018, background): “Indeed, event cameras present many advantages in all tasks related to motion estimation [2, 3, 4, 5, 6].”
Unsupervised Event-Based Learning of Optical Flow, Depth, and Egomotion (2018, background): “[15] similarly build a 3D map, which they localize from using the events.”
End-to-End Learning of Representations for Asynchronous Event-Based Data (2019, neutral): “Despite their differences, both paradigms have been successfully applied on various vision tasks, including tracking [19, 21, 40, 42], depth estimation [3, 52, 67], visual odometry [27, 54, 57, 66], recognition [29, 44], and optical flow estimation [7, 69].”