PL-EVIO: Robust Monocular Event-based Visual Inertial Odometry with
Point and Line Features
- URL: http://arxiv.org/abs/2209.12160v2
- Date: Tue, 26 Sep 2023 09:46:23 GMT
- Title: PL-EVIO: Robust Monocular Event-based Visual Inertial Odometry with
Point and Line Features
- Authors: Weipeng Guan, Peiyu Chen, Yuhan Xie, Peng Lu
- Abstract summary: Event cameras are motion-activated sensors that capture pixel-level illumination changes instead of the intensity image with a fixed frame rate.
We propose a robust, high-accurate, and real-time optimization-based monocular event-based visual-inertial odometry (VIO) method.
- Score: 3.6355269783970394
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Event cameras are motion-activated sensors that capture pixel-level
illumination changes instead of the intensity image with a fixed frame rate.
Compared with the standard cameras, it can provide reliable visual perception
during high-speed motions and in high dynamic range scenarios. However, event
cameras output only a little information or even noise when the relative motion
between the camera and the scene is limited, such as in a still state. While
standard cameras can provide rich perception information in most scenarios,
especially in good lighting conditions. These two cameras are exactly
complementary. In this paper, we proposed a robust, high-accurate, and
real-time optimization-based monocular event-based visual-inertial odometry
(VIO) method with event-corner features, line-based event features, and
point-based image features. The proposed method offers to leverage the
point-based features in the nature scene and line-based features in the
human-made scene to provide more additional structure or constraints
information through well-design feature management. Experiments in the public
benchmark datasets show that our method can achieve superior performance
compared with the state-of-the-art image-based or event-based VIO. Finally, we
used our method to demonstrate an onboard closed-loop autonomous quadrotor
flight and large-scale outdoor experiments. Videos of the evaluations are
presented on our project website: https://b23.tv/OE3QM6j
Related papers
- EF-3DGS: Event-Aided Free-Trajectory 3D Gaussian Splatting [76.02450110026747]
Event cameras, inspired by biological vision, record pixel-wise intensity changes asynchronously with high temporal resolution.
We propose Event-Aided Free-Trajectory 3DGS, which seamlessly integrates the advantages of event cameras into 3DGS.
We evaluate our method on the public Tanks and Temples benchmark and a newly collected real-world dataset, RealEv-DAVIS.
arXiv Detail & Related papers (2024-10-20T13:44:24Z) - Motion Segmentation for Neuromorphic Aerial Surveillance [42.04157319642197]
Event cameras offer superior temporal resolution, superior dynamic range, and minimal power requirements.
Unlike traditional frame-based sensors that capture redundant information at fixed intervals, event cameras asynchronously record pixel-level brightness changes.
We introduce a novel motion segmentation method that leverages self-supervised vision transformers on both event data and optical flow information.
arXiv Detail & Related papers (2024-05-24T04:36:13Z) - Temporal-Mapping Photography for Event Cameras [5.838762448259289]
Event cameras capture brightness changes as a continuous stream of events'' rather than traditional intensity frames.
We realize events to dense intensity image conversion using a stationary event camera in static scenes.
arXiv Detail & Related papers (2024-03-11T05:29:46Z) - EventAid: Benchmarking Event-aided Image/Video Enhancement Algorithms
with Real-captured Hybrid Dataset [55.12137324648253]
Event cameras are emerging imaging technology that offers advantages over conventional frame-based imaging sensors in dynamic range and sensing speed.
This paper focuses on five event-aided image and video enhancement tasks.
arXiv Detail & Related papers (2023-12-13T15:42:04Z) - Asynchronous Optimisation for Event-based Visual Odometry [53.59879499700895]
Event cameras open up new possibilities for robotic perception due to their low latency and high dynamic range.
We focus on event-based visual odometry (VO)
We propose an asynchronous structure-from-motion optimisation back-end.
arXiv Detail & Related papers (2022-03-02T11:28:47Z) - Event Guided Depth Sensing [50.997474285910734]
We present an efficient bio-inspired event-camera-driven depth estimation algorithm.
In our approach, we illuminate areas of interest densely, depending on the scene activity detected by the event camera.
We show the feasibility of our approach in a simulated autonomous driving sequences and real indoor environments.
arXiv Detail & Related papers (2021-10-20T11:41:11Z) - Moving Object Detection for Event-based vision using Graph Spectral
Clustering [6.354824287948164]
Moving object detection has been a central topic of discussion in computer vision for its wide range of applications.
We present an unsupervised Graph Spectral Clustering technique for Moving Object Detection in Event-based data.
We additionally show how the optimum number of moving objects can be automatically determined.
arXiv Detail & Related papers (2021-09-30T10:19:22Z) - VisEvent: Reliable Object Tracking via Collaboration of Frame and Event
Flows [93.54888104118822]
We propose a large-scale Visible-Event benchmark (termed VisEvent) due to the lack of a realistic and scaled dataset for this task.
Our dataset consists of 820 video pairs captured under low illumination, high speed, and background clutter scenarios.
Based on VisEvent, we transform the event flows into event images and construct more than 30 baseline methods.
arXiv Detail & Related papers (2021-08-11T03:55:12Z) - Learning Monocular Dense Depth from Events [53.078665310545745]
Event cameras produce brightness changes in the form of a stream of asynchronous events instead of intensity frames.
Recent learning-based approaches have been applied to event-based data, such as monocular depth prediction.
We propose a recurrent architecture to solve this task and show significant improvement over standard feed-forward methods.
arXiv Detail & Related papers (2020-10-16T12:36:23Z) - Event-based Stereo Visual Odometry [42.77238738150496]
We present a solution to the problem of visual odometry from the data acquired by a stereo event-based camera rig.
We seek to maximize thetemporal consistency of stereo event-based data while using a simple and efficient representation.
arXiv Detail & Related papers (2020-07-30T15:53:28Z)
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.