Marcel Neuhausen

Towards autonomous driving in a parking garage: Vehicle localization and tracking using environment-embedded LIDAR sensors

In this paper, we propose a new approach for localization and tracking of a vehicle in a parking garage, based on environment-embedded LIDAR sensors. In particular, we present an integration of data from multiple sensors, allowing to track vehicles in a common, parking garage coordinate system. In order to perform detection and tracking in realtime, a combination of appropriate methods, namely a grid-based approach, a RANSAC algorithm, and a Kalman filter is proposed and evaluated. The system achieves highly confident and exact vehicle positioning. In the context of a larger framework, our approach was used as a reference system to enable autonomous driving within a parking garage. In our experiments, we showed that the proposed algorithm allows a precise vehicle localization and tracking. Our systems results were compared to human-labeled ground-truth data. Based on this comparison we prove a high accuracy with a mean lateral and longitudinal error of 6.3cm and 8.5 cm, respectively.

Comparing Image Features and Machine Learning Algorithms for Real-Time Parking Space Classification

Finding a vacant parking lot in urban areas is mostly time-consuming and not satisfying for potential visitors or customers. Efficient car-park routing systems could support drivers to find a nun occupied parking lot. Current systems detecting vacant parking lots are either very expensive due to the hardware requirement or do not provide a detailed occupancy map. In this paper, we propose a video-based system for low-cost parking space classification. A wide-angle lens camera is used in combination with a desktop computer. We evaluate image features and machine learning algorithms to determine the occupancy of parking lots. Each combination of feature set and classifier was trained and tested on our dataset containing approximately 10,000 samples. We assessed the performance of all combinations of feature extraction and classification methods. Our final system, incorporating temporal filtering, reached an accuracy of 99.8 %.

On-vehicle video-based parking lot recognition with fisheye optics

The search for free parking space in a crowded car park is a time-consuming and tedious task. Todays park assistance systems provide the driver with acoustic or visual feedback when approaching an obstacle or semi-autonomously navigate the vehicle into the parking lot. However, finding a free parking lot is usually left to the driver. In this paper, we address this search problem via video sensors only. This can be used as a help to the driver to quickly pass a parking deck and, more important, can be regarded as a cornerstone to fully autonomously parking vehicles.

A Cascaded Classifier Approach to Window Detection in Facade Images

A major part of recent developments in civil engineering in the urban context evolved around building and city models. Especially for a precise risk assessment of damages to existing buildings induced by ground movements, accurate models are inevitable. Beside the shape of a building, the focus is also on components compromising a building’s stiffness. Particularly, by including wall openings such as windows into risk analyses, these can be improved to provide more reliable predictions. However, most publicly available data sources only provide simple blockmodels of existing buildings sometimes extended by roof shapes. As a consequence, any information concerning the windows of a building must be integrated into the model using other data sources. Whereas numerous approaches address the refinement of building shapes, their windows and other components are commonly disregarded. Although cascaded classifiers already turned out to yield good results in general and applying them to window detection seems promising, such approaches are yet insufficient to reliably extend building models. Drawing on previous findings, we present an approach to window detection in facade images satisfying the needs of risk assessment analyses. Our detection system combines a soft cascaded classifier consisting of thresholded Haar-like features with a sliding window detector extracting image patches for classification. The soft cascaded design improves the detection rate over previously made approaches while coincidentally reducing the amount of required features. Further, we evaluate the effect of a rectified dataset on the classification results compared to its counterpart with images taken from varying angles.

Park marking-based vehicle self-localization with a fisheye topview system

Accurately self-localizing a vehicle is of high importance as it allows to robustify nearly all modern driver assistance functionality, e.g., lane keeping and coordinated autonomous driving maneuvers. We examine vehicle self-localization relying only on video sensors, in particular, a system of four fisheye cameras providing a view surrounding the car, a setup currently growing popular in upper-class cars. The presented work aims at an autonomous parking scenario. The method is based on park markings as orientation marks since they can be found in nearly every parking deck and require only little additional preparation. Our contribution is twofold: (1) we present a new real-time capable image processing pipeline for topview systems extracting park markings and show how to obtain a reliable and accurate ego pose and ego motion estimation given a coarse pose as starting point. (2) The aptitude of this often neglected sensor array for vehicle self-localization is demonstrated. Experimental evaluation yields a precision of 0.15

Improved Window Detection in Facade Images

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Window detection in facade images for risk assessment in tunneling

± 0.18 m and 2.01