For autonomous driving in urban environments it is usually assumed that the road is flat. To drive off-road, however, we need a more sophisticated model of the ground surface. While previous work is mapping the terrain along with static obstacles, we propose to separate the tasks and introduce a new approach to probabilistic terrain estimation. It combines recursive Gaussian state estimation with a subsequent maximum a posteriori estimation. This allows us to efficiently accumulate obtained measurements and at the same time get a probabilistic terrain estimate based on a geometric terrain model. This way, also (measurement) uncertainties as well as inter- and extrapolation to unobserved areas are handled stochastically correct. We demonstrate the effectiveness and real-time capability of our approach using real-world data.

We propose a modular framework for 3D tracking not only of paved roads but also of dirt roads. It is based on recursive state estimation of lane boundary points connected by clothoid pieces. While our tracking is flexible to integrate every kind of measurement, we specifically propose two image-based measurements. They combine traditional with modern computer vision: On the one hand, we show how to use directed edge detection to robustly measure road and lane boundaries. On the other hand, we introduce a innovative CNN-based measurement utilizing the self-similarity of (dirt) road areas. We demonstrate the performance of our approach in challenging scenarios. On a marked road, we achieve a median error of 0.13 m for the ego lane's boundaries in 25 m look-ahead. A difficult dirt road can also be tracked reliably with a lookahead length of 25 m, resulting in a median error of 0.3 m. The tracking, as well as both measurements, are real-time capable.

Fiducial markers like AprilTags play an important role in robotics, e.g., for the calibration of cameras or the localization of robots. One of the most important properties of an algorithm for detecting such tags is its localization accuracy.In this paper, we present the results of an extensive comparison of four freely available libraries capable of detecting AprilTags, namely AprilTag 3, AprilTags C++, ArUco as standalone libraries, and the OpenCV algorithm based on ArUco. The focus of the comparison is on localization accuracy, but the processing time is also examined. Besides working with pure tags, their extension to checkerboard corners is investigated.In addition, we present two new post-processing techniques. Firstly, a method that can filter out very inaccurate detections resulting from partial border occlusion, and secondly a new highly accurate method for edge refinement. With this we achieve a median pixel error of 0.017 px, compared to 0.17 px for standard OpenCV corner refinement.The dataset used for the evaluation, as well as the developed post-processing techniques, are made publicly available to encourage further comparison and improvement of the detection libraries.

In this work, we propose an extension of the Semi-Global Matching framework for three images from a triplet-stereo rig consisting of a horizontal and vertical camera pair. After calculating the matching costs separately for both image pairs, these are merged at cost level using cubic spline interpolation. For cost values near the left/bottom image boundaries, we propose an advanced weighting strategy. Subsequently, the fused matching can be used directly for the cost aggregation and disparity estimation.The benefits of the proposed fusion strategy are demonstrated by an evaluation based on synthetic and real-world data. To encourage further comparisons on triple stereo algorithms, the dataset used for evaluation is made publicly available.

In this paper, we propose to complement a horizontally aligned stereo rig with a vertically aligned one. Based on artificial and real camera images we show that certain image structures can be detected by only one of the setups. Actually, the depth of structures with significant gradients in just one direction can only be measured using cameras with an offset in the same direction. Thus, the joint usage of both setups allows for significantly increased robustness and reliability. Moreover, we propose an approach for the fusion of two disparity images gained from two stereo camera pairs with one common camera. The algorithm uses an approximation of the actually measured matching cost function. Thus it takes into account the present image structures and the capabilities of each particular stereo setup. Due to its mathematical simplicity, it can be computed within 1 ms for images with a size of 1164 × 335 pixel. The performance of the fusion is demonstrated using different real-world scenarios.

Obstacle avoidance is a key feature for automotive navigation that requires an accurate representation of the environment. In the field of visual perception this task has often been addressed with stereo algorithms that try to obtain a depth map of the environment via disparity calculations on a single pair of images. These algorithms do not exploit that especially in automotive scenarios the fields of view between two consecutive frames have large overlapping regions. Instead, the disparity map is computed from scratch for each stereo frame and no information is propagated from one frame to the next. Since monocular image processing has long benefited from recursive estimation techniques, such as the 4D Approach, this paper presents a novel recursive automotive stereo algorithm, called RAS. RAS internally maintains a list of recursively estimated 3D points that are continuously updated based on the vehicle's movement and measurements in the current stereo frame. We show that RAS not only preserves the knowledge of the environment across frames, but also accounts for measurement modalities and is robust against faulty or even missing measurements.