890 research outputs found
Stereo Event-based Visual-Inertial Odometry
Event-based cameras are new type vision sensors whose pixels work
independently and respond asynchronously to brightness change with microsecond
resolution, instead of providing standard intensity frames. Compared with
traditional cameras, event-based cameras have low latency, no motion blur, and
high dynamic range (HDR), which provide possibilities for robots to deal with
some challenging scenes. We propose a visual-inertial odometry for stereo
event-based cameras based on Error-State Kalman Filter (ESKF). The visual
module updates the pose relies on the edge alignment of a semi-dense 3D map to
a 2D image, and the IMU module updates pose by median integral. We evaluate our
method on public datasets with general 6-DoF motion and compare the results
against ground truth. We show that our proposed pipeline provides improved
accuracy over the result of the state-of-the-art visual odometry for stereo
event-based cameras, while running in real-time on a standard CPU
(low-resolution cameras). To the best of our knowledge, this is the first
published visual-inertial odometry for stereo event-based cameras
Event-based Vision: A Survey
Event cameras are bio-inspired sensors that differ from conventional frame
cameras: Instead of capturing images at a fixed rate, they asynchronously
measure per-pixel brightness changes, and output a stream of events that encode
the time, location and sign of the brightness changes. Event cameras offer
attractive properties compared to traditional cameras: high temporal resolution
(in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low
power consumption, and high pixel bandwidth (on the order of kHz) resulting in
reduced motion blur. Hence, event cameras have a large potential for robotics
and computer vision in challenging scenarios for traditional cameras, such as
low-latency, high speed, and high dynamic range. However, novel methods are
required to process the unconventional output of these sensors in order to
unlock their potential. This paper provides a comprehensive overview of the
emerging field of event-based vision, with a focus on the applications and the
algorithms developed to unlock the outstanding properties of event cameras. We
present event cameras from their working principle, the actual sensors that are
available and the tasks that they have been used for, from low-level vision
(feature detection and tracking, optic flow, etc.) to high-level vision
(reconstruction, segmentation, recognition). We also discuss the techniques
developed to process events, including learning-based techniques, as well as
specialized processors for these novel sensors, such as spiking neural
networks. Additionally, we highlight the challenges that remain to be tackled
and the opportunities that lie ahead in the search for a more efficient,
bio-inspired way for machines to perceive and interact with the world
Event-based Visual Odometry with Full Temporal Resolution via Continuous-time Gaussian Process Regression
Event-based cameras asynchronously capture individual visual changes in a
scene. This makes them more robust than traditional frame-based cameras to
highly dynamic motions and poor illumination. It also means that every
measurement in a scene can occur at a unique time.
Handling these different measurement times is a major challenge of using
event-based cameras. It is often addressed in visual odometry (VO) pipelines by
approximating temporally close measurements as occurring at one common time.
This grouping simplifies the estimation problem but sacrifices the inherent
temporal resolution of event-based cameras.
This paper instead presents a complete stereo VO pipeline that estimates
directly with individual event-measurement times without requiring any grouping
or approximation. It uses continuous-time trajectory estimation to maintain the
temporal fidelity and asynchronous nature of event-based cameras through
Gaussian process regression with a physically motivated prior. Its performance
is evaluated on the MVSEC dataset, where it achieves 7.9e-3 and 5.9e-3 RMS
relative error on two independent sequences, outperforming the existing
publicly available event-based stereo VO pipeline by two and four times,
respectively.Comment: Submitted to IEEE Robotics and Automation Letters (RA-L). Manuscript
#23-1314. 8 pages, 4 figure
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
- …