1,617 research outputs found
3D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle Detection
Cameras are a crucial exteroceptive sensor for self-driving cars as they are
low-cost and small, provide appearance information about the environment, and
work in various weather conditions. They can be used for multiple purposes such
as visual navigation and obstacle detection. We can use a surround multi-camera
system to cover the full 360-degree field-of-view around the car. In this way,
we avoid blind spots which can otherwise lead to accidents. To minimize the
number of cameras needed for surround perception, we utilize fisheye cameras.
Consequently, standard vision pipelines for 3D mapping, visual localization,
obstacle detection, etc. need to be adapted to take full advantage of the
availability of multiple cameras rather than treat each camera individually. In
addition, processing of fisheye images has to be supported. In this paper, we
describe the camera calibration and subsequent processing pipeline for
multi-fisheye-camera systems developed as part of the V-Charge project. This
project seeks to enable automated valet parking for self-driving cars. Our
pipeline is able to precisely calibrate multi-camera systems, build sparse 3D
maps for visual navigation, visually localize the car with respect to these
maps, generate accurate dense maps, as well as detect obstacles based on
real-time depth map extraction
Multi-Camera Visual-Inertial Simultaneous Localization and Mapping for Autonomous Valet Parking
Localization and mapping are key capabilities for self-driving vehicles. In
this paper, we build on Kimera and extend it to use multiple cameras as well as
external (eg wheel) odometry sensors, to obtain accurate and robust odometry
estimates in real-world problems. Additionally, we propose an effective scheme
for closing loops that circumvents the drawbacks of common alternatives based
on the Perspective-n-Point method and also works with a single monocular
camera. Finally, we develop a method for dense 3D mapping of the free space
that combines a segmentation network for free-space detection with a
homography-based dense mapping technique. We test our system on photo-realistic
simulations and on several real datasets collected on a car prototype developed
by the Ford Motor Company, spanning both indoor and outdoor parking scenarios.
Our multi-camera system is shown to outperform state-of-the art open-source
visual-inertial-SLAM pipelines (Vins-Fusion, ORB-SLAM3), and exhibits an
average trajectory error under 1% of the trajectory length across more than 8km
of distance traveled (combined across all datasets). A video showcasing the
system is available at: youtu.be/H8CpzDpXOI8
Full-automatic recognition of various parking slot markings using a hierarchical tree structure
A full-automatic method for recognizing parking slot markings is proposed. The proposed method recognizes various types of parking slot markings by modeling them as a hierarchical tree structure. This method mainly consists of two processes: bottom-up and top-down. First, the bottom-up process climbs up the hierarchical tree structure to excessively generate parking slot candidates so as not to lose the correct slots. This process includes corner detection, junction and slot generation, and type selection procedures. After that, the top-down process confirms the final parking slots by eliminating falsely generated slots, junctions, and corners based on the properties of the parking slot marking type by climbing down the hierarchical tree structure. The proposed method was evaluated in 608 real-world parking situations encompassing a variety of different parking slot markings. The experimental result reveals that the proposed method outperforms the previous semiautomatic method while requiring a small amount of computational costs even though it is fully automatic
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
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