3,651 research outputs found
Ego-motion and Surrounding Vehicle State Estimation Using a Monocular Camera
Understanding ego-motion and surrounding vehicle state is essential to enable
automated driving and advanced driving assistance technologies. Typical
approaches to solve this problem use fusion of multiple sensors such as LiDAR,
camera, and radar to recognize surrounding vehicle state, including position,
velocity, and orientation. Such sensing modalities are overly complex and
costly for production of personal use vehicles. In this paper, we propose a
novel machine learning method to estimate ego-motion and surrounding vehicle
state using a single monocular camera. Our approach is based on a combination
of three deep neural networks to estimate the 3D vehicle bounding box, depth,
and optical flow from a sequence of images. The main contribution of this paper
is a new framework and algorithm that integrates these three networks in order
to estimate the ego-motion and surrounding vehicle state. To realize more
accurate 3D position estimation, we address ground plane correction in
real-time. The efficacy of the proposed method is demonstrated through
experimental evaluations that compare our results to ground truth data
available from other sensors including Can-Bus and LiDAR
Fast, Accurate Thin-Structure Obstacle Detection for Autonomous Mobile Robots
Safety is paramount for mobile robotic platforms such as self-driving cars
and unmanned aerial vehicles. This work is devoted to a task that is
indispensable for safety yet was largely overlooked in the past -- detecting
obstacles that are of very thin structures, such as wires, cables and tree
branches. This is a challenging problem, as thin objects can be problematic for
active sensors such as lidar and sonar and even for stereo cameras. In this
work, we propose to use video sequences for thin obstacle detection. We
represent obstacles with edges in the video frames, and reconstruct them in 3D
using efficient edge-based visual odometry techniques. We provide both a
monocular camera solution and a stereo camera solution. The former incorporates
Inertial Measurement Unit (IMU) data to solve scale ambiguity, while the latter
enjoys a novel, purely vision-based solution. Experiments demonstrated that the
proposed methods are fast and able to detect thin obstacles robustly and
accurately under various conditions.Comment: Appeared at IEEE CVPR 2017 Workshop on Embedded Visio
Probabilistic RGB-D Odometry based on Points, Lines and Planes Under Depth Uncertainty
This work proposes a robust visual odometry method for structured
environments that combines point features with line and plane segments,
extracted through an RGB-D camera. Noisy depth maps are processed by a
probabilistic depth fusion framework based on Mixtures of Gaussians to denoise
and derive the depth uncertainty, which is then propagated throughout the
visual odometry pipeline. Probabilistic 3D plane and line fitting solutions are
used to model the uncertainties of the feature parameters and pose is estimated
by combining the three types of primitives based on their uncertainties.
Performance evaluation on RGB-D sequences collected in this work and two public
RGB-D datasets: TUM and ICL-NUIM show the benefit of using the proposed depth
fusion framework and combining the three feature-types, particularly in scenes
with low-textured surfaces, dynamic objects and missing depth measurements.Comment: Major update: more results, depth filter released as opensource, 34
page
Vision-based localization methods under GPS-denied conditions
This paper reviews vision-based localization methods in GPS-denied
environments and classifies the mainstream methods into Relative Vision
Localization (RVL) and Absolute Vision Localization (AVL). For RVL, we discuss
the broad application of optical flow in feature extraction-based Visual
Odometry (VO) solutions and introduce advanced optical flow estimation methods.
For AVL, we review recent advances in Visual Simultaneous Localization and
Mapping (VSLAM) techniques, from optimization-based methods to Extended Kalman
Filter (EKF) based methods. We also introduce the application of offline map
registration and lane vision detection schemes to achieve Absolute Visual
Localization. This paper compares the performance and applications of
mainstream methods for visual localization and provides suggestions for future
studies.Comment: 32 pages, 15 figure
Fast, Autonomous Flight in GPS-Denied and Cluttered Environments
One of the most challenging tasks for a flying robot is to autonomously
navigate between target locations quickly and reliably while avoiding obstacles
in its path, and with little to no a-priori knowledge of the operating
environment. This challenge is addressed in the present paper. We describe the
system design and software architecture of our proposed solution, and showcase
how all the distinct components can be integrated to enable smooth robot
operation. We provide critical insight on hardware and software component
selection and development, and present results from extensive experimental
testing in real-world warehouse environments. Experimental testing reveals that
our proposed solution can deliver fast and robust aerial robot autonomous
navigation in cluttered, GPS-denied environments.Comment: Pre-peer reviewed version of the article accepted in Journal of Field
Robotic
RT-SLAM: A Generic and Real-Time Visual SLAM Implementation
This article presents a new open-source C++ implementation to solve the SLAM
problem, which is focused on genericity, versatility and high execution speed.
It is based on an original object oriented architecture, that allows the
combination of numerous sensors and landmark types, and the integration of
various approaches proposed in the literature. The system capacities are
illustrated by the presentation of an inertial/vision SLAM approach, for which
several improvements over existing methods have been introduced, and that copes
with very high dynamic motions. Results with a hand-held camera are presented.Comment: 10 page
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