Many tasks performed by autonomous vehicles such as road marking detection, object tracking, and path planning are simpler in bird's-eye view. Hence, Inverse Perspective Mapping (IPM) is often applied to remove the perspective effect from a vehicle's front-facing camera and to remap its images into a 2D domain, resulting in a top-down view. Unfortunately, however, this leads to unnatural blurring and stretching of objects at further distance, due to the resolution of the camera, limiting applicability. In this paper, we present an adversarial learning approach for generating a significantly improved IPM from a single camera image in real time. The generated bird'seye-view images contain sharper features (e.g, road markings) and a more homogeneous illumination, while (dynamic) objects are automatically removed from the scene, thus revealing the underlying road layout in an improved fashion. We demonstrate our framework using real-world data from the Oxford Robot-Car Dataset and show that scene understanding tasks directly benefit from our boosted IPM approach.
Several works have tried to adjust for inaccuracies caused by invalidity of the first two assumptions. The authors of @cite_34 @cite_41 used vanishing point detection, @cite_7 estimated the slope of the road according to the lane markings, and @cite_14 employed motion estimation obtained from SLAM. Invalidity of the third assumption is tackled in @cite_18 by using a laser scanner to exclude obstacles from being transformed to IPM. Another approach @cite_16 @cite_9 @cite_4 creates a look up table for all pixels, by taking into account the distance of objects on the road surface, in order to reduce artefacts at further distance. However, these methods generally assume simple environments (i.e. highway). Contrarily, we learn a non-linear mapping more suited for urban scenes.
@cite_18 @cite_14 @cite_4 @cite_7 @cite_41 @cite_9 @cite_16 @cite_34
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Over the past years, inverse perspective mapping has been successfully applied to several problems in the field of Intelligent Transportation Systems. In brief, the method consists of mapping images to a new coordinate system where perspective effects are removed. The removal of perspective associated effects facilitates road and obstacle detection and also assists in free space estimation. There is, however, a significant limitation in the inverse perspective mapping: the presence of obstacles on the road disrupts the effectiveness of the mapping. The current paper proposes a robust solution based on the use of multimodal sensor fusion. Data from a laser range finder is fused with images from the cameras, so that the mapping is not computed in the regions where obstacles are present. As shown in the results, this considerably improves the effectiveness of the algorithm and reduces computation time when compared with the classical inverse perspective mapping. Furthermore, the proposed approach is also able to cope with several cameras with different lenses or image resolutions, as well as dynamic viewpoints. This paper proposes an adaptive Inverse Perspective Mapping (IPM) algorithm to obtain accurate bird's-eye view images from the sequential images of forward looking cameras. These images are often distorted by the motion of the vehicle; even a small motion can cause a substantial effect on bird'seye view images. In this paper, we propose an adaptive model for the IPM to accurately transform camera images to bird'seye view images by using motion information. Using motion derived from the monocular visual simultaneous localization and mapping (SLAM), experimental result shows that the proposed approaches can provide stable bird's-eye view images, even with large motion during the drive. In this paper, the authors examine the issue of Inverse Perspective Mapping (IPM) which removes the perspective effect from acquired images that are associated with autonomous driving systems. They in turn present an Extended IPM (EIPM) which is based on stereo image processing and which is used to update the road slope ahead of the vehicle. The EIPM removes the assumption of a flat road ahead of the vehicle and allows for the recovery of road texture even in the presence of a slope. The authors describe how this technique is applied to synthetic images and how it has been integrated into the GOLD system on the ARGO autonomous vehicle. This paper proposes the Top-View Transformation Model for image coordinate transformation, which involves transforming a perspective projection image into its corresponding bird's eye vision. A fitting parameters searching algorithm estimates the parameters that are used to transform the coordinates from the source image. Using this approach, it is not necessary to provide any interior and exterior orientation parameters of the camera. The designed car parking assistant system can be installed at the rear end of the car, providing the driver with a clearer image of the area behind the car. The processing time can be reduced by storing and using the transformation matrix estimated from the first image frame for a sequence of video images. The transformation matrix can be stored as the Matrix Mapping Table, and loaded into the embedded platform to perform the transformation. Experimental results show that the proposed approaches can provide a clearer and more accurate bird's eye view to the vehicle driver. In this work, a new inverse perspective mapping (IPM) technique is proposed based on a robust estimation of the vanishing point, which provide bird-view images of the road, so that facilitating the tasks of road modeling and vehicle detection and tracking. This new approach has been design to cope with the instability that cameras mounted on a moving vehicle suffer. The estimation of the vanishing point relies on a novel and efficient feature extraction strategy, which segmentates the lane markings of the images by combining a histogram-based segmentation with temporal and frequency filtering. Then, the vanishing point of each image is stabilized by means of a temporal filtering along the estimates of previous images. In a last step, the IPM image is computed based on the stabilized vanishing point. Tests have been carried out on several long video sequences captured from cameras inside a vehicle being driven along highways and local roads, with different illumination and weather conditions, presence of shadows, occluding vehicles, and slope changes. Results have shown a significant improvement in terms of lane width constancy and parallelism between lane markings over non-stabilized IPM algorithms.