Lidar scan feature for localization with highly precise 3-D map

Vehicle self-localization is an important and challenging issue in current driving assistance and autonomous driving research activities. This paper mainly investigates two kinds of methods for vehicle self-localization: active sensor based and passive sensor based. Active sensor based localization was firstly proposed for robot localization, and was introduced into autonomous driving recently. The Simultaneous Localization and Mapping (SLAM) techniques is the representative in active sensor based localization. The passive sensor based localization technologies are categorized and explained based on the type of sensors, Global Navigation Satellite System (GNSS), inertial sensors and cameras. In this paper, researches utilizing active sensors and passive sensors for autonomous vehicles are reviewed extensively. Finally, our challenges on self-localization in urban canyon by the system integration of passive sensors is introduced. GNSS error has been improved for the purpose of the self-localization in urban canyon. The performance of the proposed method would suggest possible solution autonomous vehicles which makes use of passive sensors more.

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“…This map has been subsequently used to realize an autonomous parking behavior (27) . ( at Toyota Technological Institute (12) .…”

Section: Vision Based Object Detectionmentioning

confidence: 99%

Autonomous Vehicle Technologies :Localization and Mapping

俊介

艶磊

立達

2015

Fundamentals Review

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“…Instead of using specific landmarks, which means that only a small percentage of the reflected laser beams is taken into account, several approaches make use of the entire LiDAR data. (Yoneda et al, 2014) use the generated 3D point cloud for localization by matching it to a 3D reference cloud in real-time. One big disadvantage of this approach is that storing the reference cloud in a map requires much storage space.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Localization by Lidar Point Correlation Improved by Change Detection

Schlichting

Brenner

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:LiDAR sensors are proven sensors for accurate vehicle localization. Instead of detecting and matching features in the LiDAR data, we want to use the entire information provided by the scanners. As dynamic objects, like cars, pedestrians or even construction sites could lead to wrong localization results, we use a change detection algorithm to detect these objects in the reference data. If an object occurs in a certain number of measurements at the same position, we mark it and every containing point as static. In the next step, we merge the data of the single measurement epochs to one reference dataset, whereby we only use static points. Further, we also use a classification algorithm to detect trees. For the online localization of the vehicle, we use simulated data of a vertical aligned automotive LiDAR sensor. As we only want to use static objects in this case as well, we use a random forest classifier to detect dynamic scan points online. Since the automotive data is derived from the LiDAR Mobile Mapping System, we are able to use the labelled objects from the reference data generation step to create the training data and further to detect dynamic objects online. The localization then can be done by a point to image correlation method using only static objects. We achieved a localization standard deviation of about 5 cm (position) and 0.06° (heading), and were able to successfully localize the vehicle in about 93 % of the cases along a trajectory of 13 km in Hannover,

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“…On the other hands, Noyer, et al (2008) used a DGPS, other inertial sensors and a lane detection system mounted on their test car in order to automatically generate a high precision map for ADAS. Yoneda, et al (2014) also focused on features from a laser scanner data for localization with highly precise 3D map.…”

Section: Introductionmentioning

confidence: 99%

Classification of Lidar Data for Generating a High-Precision Roadway Map

Jeong

Lee

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Generating of a highly precise map grows up with development of autonomous driving vehicles. The highly precise map includes a precision of centimetres level unlike an existing commercial map with the precision of meters level. It is important to understand road environments and make a decision for autonomous driving since a robust localization is one of the critical challenges for the autonomous driving car. The one of source data is from a Lidar because it provides highly dense point cloud data with three dimensional position, intensities and ranges from the sensor to target. In this paper, we focus on how to segment point cloud data from a Lidar on a vehicle and classify objects on the road for the highly precise map. In particular, we propose the combination with a feature descriptor and a classification algorithm in machine learning. Objects can be distinguish by geometrical features based on a surface normal of each point. To achieve correct classification using limited point cloud data sets, a Support Vector Machine algorithm in machine learning are used. Final step is to evaluate accuracies of obtained results by comparing them to reference data The results show sufficient accuracy and it will be utilized to generate a highly precise road map.

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Lidar scan feature for localization with highly precise 3-D map

Cited by 93 publications

References 13 publications

Autonomous Vehicle Technologies :Localization and Mapping

Autonomous Vehicle Technologies :Localization and Mapping

Vehicle Localization by Lidar Point Correlation Improved by Change Detection

Classification of Lidar Data for Generating a High-Precision Roadway Map

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