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Analyse der Sensorabhängigkeit einer LiDAR-basierten Objekterkennung mit neuronalen Netzen

  • The task of object detection in the automotive sector can be performed by evaluating various sensor data. The evaluation of LiDAR data for the detection of objects is a special challenge for which systems with neural networks can be used. These neural networks are trained by means of a data set. If you want to use the net with your own recordings or another data set, it is important to know how well these systems work in combination with data from another sensor. This allows the results to be estimated in advance and compared with the results of previous experiments. In this work the sensor dependence of a LiDAR based object recognition with neural networks will be analysed. The detector used in this work is PointRCNN [1], which was designed for the KITTI dataset [2]. To check the sensor dependency, the ’AEV Autonomous Driving Dataset’ (A2D2) dataset [3] was selected as a further dataset. After an introduction to PointRCNN and its functionality, the data of both datasets are analysed. Then the data of the second dataset will be ported into the format of the KITTI dataset so that they can be used with PointRCNN. Through experiments with varying combinations of training and validation data it shall be investigated to what extent trained models can be transferred to other sensor data or datasets. Therefore, it shall be investigated how strong the dependence of the detector (PointRCNN) on the used sensors is. The results show that PointRCNN can be evaluated with a different dataset than the training dataset while still being able to detect objects. The point density of the datasets plays a decisive role for the quality of the detection. Therefore it can be said that PointRCNN has a sensor dependency that varies with the nature of the point cloud and its density. Keywords: LiDAR data, 3D object recognition, laser scanner, sensor dependency, PointRCNN, PointNet++, PointNet, KITTI Dataset, AEV Autonomous Driving Dataset, A2D2 Dataset

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Author:Niclas Hüwe
Document Type:Bachelor Thesis
Year of Completion:2020
Release Date:2020/08/24
Institutes:Fachbereich 1 - Institut Informatik
DDC class:300 Sozialwissenschaften / 330 Wirtschaft
Licence (German):License LogoNo Creative Commons