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首页> 外文会议>2012 IEEE International Conference on Vehicular Electronics and Safety >Vehicle geo-localization using IMM-UKF multi-sensor data fusion based on virtual 3D city model as a priori information
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Vehicle geo-localization using IMM-UKF multi-sensor data fusion based on virtual 3D city model as a priori information

机译：基于虚拟3D城市模型作为先验信息的IMM-UKF多传感器数据融合的车辆地理定位

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The major contribution of this paper concerns vehicle geo-localization in urban environment by integrating a new source of information that is a virtual 3D city model. The 3D model provides a realistic representation of the vehicle's navigation environment. To optimize the performance of vehicle geo-localization system, several sources of information are used for their complementarity and redundancy: a GPS receiver, proprioceptive sensors (odometers and gyrometer), exteroceptive sensors (video camera or laser scanner) and a virtual 3D city model as a priori information. The proprioceptive sensors allow to continuously estimate the dead-reckoning position and orientation of the vehicle. Moreover, two concepts based on 3D virtual model and exteroceptive sensors could be envisaged to compensate the drift of the dead-reckoning localization when GPS measurements are unavailable for a long time. The first proposed approach is based on the matching between the virtual image extracted from the 3D city model and the real image acquired by the camera. This observation construction is composed of two major parts. The first part consists in detecting and matching the feature points of the real and virtual images. Three features are compared: Harris corner, SIFT (Scale Invariant Feature Transform) and SURF (Speed Up Robust Features). The second part is the pose computation using POSIT algorithm and the previously matched features set. The second approach uses an on-board horizontal laser scanner which provides a set of distances. This set of distances (real laser scan data) is matched with depth information of virtual laser scan data obtained using the virtual 3D city model which is managed in real-time by a 3D Geographical Information System (3D-GIS). GPS measurements, proprioceptive sensors based pose estimation, and camera/3D model based pose estimation are integrated in IMM UKF data fusion formalism. The developed approaches have been tested on a real sequence and the obta- ned results proved the feasibility of the approach.
机译：本文的主要贡献涉及通过集成作为虚拟3D城市模型的新信息源，在城市环境中对车辆进行地理定位。 3D模型提供了车辆导航环境的逼真的表示。为了优化车辆地理定位系统的性能，使用了多种信息源来补充和补充信息：GPS接收器，本体感受传感器（里程表和陀螺仪），外部感受传感器（摄像机或激光扫描仪）和虚拟3D城市模型作为先验信息。本体感受传感器允许连续地估计车辆的死守位置和方向。此外，可以设想基于3D虚拟模型和外部感知传感器的两个概念，以在长时间无法使用GPS测量时补偿死海定位局域性的漂移。首先提出的方法是基于从3D城市模型中提取的虚拟图像与相机获取的真实图像之间的匹配。此观察构造由两个主要部分组成。第一部分包括检测和匹配真实和虚拟图像的特征点。比较了三个特征：Harris角，SIFT（尺度不变特征变换）和SURF（加速鲁棒特征）。第二部分是使用POSIT算法和先前匹配的特征集进行姿势计算。第二种方法是使用车载水平激光扫描仪，该扫描仪提供一组距离。这组距离（实际激光扫描数据）与使用虚拟3D城市模型获得的虚拟激光扫描数据的深度信息匹配，该虚拟3D城市模型由3D地理信息系统（3D-GIS）实时管理。 GPS测量，基于本体感受传感器的姿势估计以及基于相机/ 3D模型的姿势估计已集成在IMM UKF数据融合形式中。已对开发的方法进行了真实的序列测试，结果表明该方法是可行的。

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