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An evaluation framework for stereo-based driver assistance
This is the post-print version of the Article - Copyright @ 2012 Springer VerlagThe accuracy of stereo algorithms or optical flow methods is commonly assessed by comparing the results against the Middlebury
database. However, equivalent data for automotive or robotics applications
rarely exist as they are difficult to obtain. As our main contribution, we introduce an evaluation framework tailored for stereo-based driver assistance able to deliver excellent performance measures while
circumventing manual label effort. Within this framework one can combine several ways of ground-truthing, different comparison metrics, and use large image databases.
Using our framework we show examples on several types of ground truthing techniques: implicit ground truthing (e.g. sequence recorded without a crash occurred), robotic vehicles with high precision sensors, and to a small extent, manual labeling. To show the effectiveness of our evaluation framework we compare three different stereo algorithms on
pixel and object level. In more detail we evaluate an intermediate representation
called the Stixel World. Besides evaluating the accuracy of the Stixels, we investigate the completeness (equivalent to the detection rate) of the StixelWorld vs. the number of phantom Stixels. Among many findings, using this framework enables us to reduce the number of phantom Stixels by a factor of three compared to the base parametrization. This base parametrization has already been optimized by test driving vehicles for distances exceeding 10000 km
Review and classification of vision-based localisation techniques in unknown environments
International audienceThis study presents a review of the state-of-the-art and a novel classification of current vision-based localisation techniques in unknown environments. Indeed, because of progresses made in computer vision, it is now possible to consider vision-based systems as promising navigation means that can complement traditional navigation sensors like global navigation satellite systems (GNSSs) and inertial navigation systems. This study aims to review techniques employing a camera as a localisation sensor, provide a classification of techniques and introduce schemes that exploit the use of video information within a multi-sensor system. In fact, a general model is needed to better compare existing techniques in order to decide which approach is appropriate and which are the innovation axes. In addition, existing classifications only consider techniques based on vision as a standalone tool and do not consider video as a sensor among others. The focus is addressed to scenarios where no a priori knowledge of the environment is provided. In fact, these scenarios are the most challenging since the system has to cope with objects as they appear in the scene without any prior information about their expected position
A New Wave in Robotics: Survey on Recent mmWave Radar Applications in Robotics
We survey the current state of millimeterwave (mmWave) radar applications in
robotics with a focus on unique capabilities, and discuss future opportunities
based on the state of the art. Frequency Modulated Continuous Wave (FMCW)
mmWave radars operating in the 76--81GHz range are an appealing alternative to
lidars, cameras and other sensors operating in the near visual spectrum. Radar
has been made more widely available in new packaging classes, more convenient
for robotics and its longer wavelengths have the ability to bypass visual
clutter such as fog, dust, and smoke. We begin by covering radar principles as
they relate to robotics. We then review the relevant new research across a
broad spectrum of robotics applications beginning with motion estimation,
localization, and mapping. We then cover object detection and classification,
and then close with an analysis of current datasets and calibration techniques
that provide entry points into radar research.Comment: 19 Pages, 11 Figures, 2 Tables, TRO Submission pendin
Visual Semantic SLAM with Landmarks for Large-Scale Outdoor Environment
Semantic SLAM is an important field in autonomous driving and intelligent
agents, which can enable robots to achieve high-level navigation tasks, obtain
simple cognition or reasoning ability and achieve language-based
human-robot-interaction. In this paper, we built a system to creat a semantic
3D map by combining 3D point cloud from ORB SLAM with semantic segmentation
information from Convolutional Neural Network model PSPNet-101 for large-scale
environments. Besides, a new dataset for KITTI sequences has been built, which
contains the GPS information and labels of landmarks from Google Map in related
streets of the sequences. Moreover, we find a way to associate the real-world
landmark with point cloud map and built a topological map based on semantic
map.Comment: Accepted by 2019 China Symposium on Cognitive Computing and Hybrid
Intelligence(CCHI'19
Simultaneous Localization and Mapping (SLAM) for Autonomous Driving: Concept and Analysis
The Simultaneous Localization and Mapping (SLAM) technique has achieved astonishing progress over the last few decades and has generated considerable interest in the autonomous driving community. With its conceptual roots in navigation and mapping, SLAM outperforms some traditional positioning and localization techniques since it can support more reliable and robust localization, planning, and controlling to meet some key criteria for autonomous driving. In this study the authors first give an overview of the different SLAM implementation approaches and then discuss the applications of SLAM for autonomous driving with respect to different driving scenarios, vehicle system components and the characteristics of the SLAM approaches. The authors then discuss some challenging issues and current solutions when applying SLAM for autonomous driving. Some quantitative quality analysis means to evaluate the characteristics and performance of SLAM systems and to monitor the risk in SLAM estimation are reviewed. In addition, this study describes a real-world road test to demonstrate a multi-sensor-based modernized SLAM procedure for autonomous driving. The numerical results show that a high-precision 3D point cloud map can be generated by the SLAM procedure with the integration of Lidar and GNSS/INS. Online four–five cm accuracy localization solution can be achieved based on this pre-generated map and online Lidar scan matching with a tightly fused inertial system
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
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