An Image Orientation Estimation Method Based on Gradient Histogram and Point Matching

During the last decade the use of airborne multi camera systems increased significantly. The development in digital camera technology allows mounting several mid- or small-format cameras efficiently onto one platform and thus enables image capture under different angles. Those oblique images turn out to be interesting for a number of applications since lateral parts of elevated objects, like buildings or trees, are visible. However, occlusion or illumination differences might challenge image processing. From an image orientation point of view those multi-camera systems bring the advantage of a better ray intersection geometry compared to nadir-only image blocks. On the other hand, varying scale, occlusion and atmospheric influences which are difficult to model impose problems to the image matching and bundle adjustment tasks. In order to understand current limitations of image orientation approaches and the influence of different parameters such as image overlap or GCP distribution, a commonly available dataset was released. The originally captured data comprises of a state-of-the-art image block with very high overlap, but in the first stage of the so-called ISPRS/EUROSDR benchmark on multi-platform photogrammetry only a reduced set of images was released. In this paper some first results obtained with this dataset are presented. They refer to different aspects like tie point matching across the viewing directions, influence of the oblique images onto the bundle adjustment, the role of image overlap and GCP distribution. As far as the tie point matching is concerned we observed that matching of overlapping images pointing to the same cardinal direction, or between nadir and oblique views in general is quite successful. Due to the quite different perspective between images of different viewing directions the standard tie point matching, for instance based on interest points does not work well. How to address occlusion and ambiguities due to different views onto objects is clearly a non-solved research problem so far. In our experiments we also confirm that the obtainable height accuracy is better when all images are used in bundle block adjustment. This was also shown in other research before and is confirmed here. Not surprisingly, the large overlap of 80/80% provides much better object space accuracy – random errors seem to be about 2-3fold smaller compared to the 60/60% overlap. A comparison of different software approaches shows that newly emerged commercial packages, initially intended to work with small frame image blocks, do perform very well.

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A Novel Pedestrian Orientation Estimation Method for Autonomous Driving

Green, Smart and Connected Transportation Systems - Lecture Notes in Electrical Engineering ◽

10.1007/978-981-15-0644-4_34 ◽

2020 ◽

pp. 431-449

Author(s):

Ming Gao ◽

LiSheng Jin ◽

Yuying Jiang ◽

Baicang Guo

Keyword(s):

Estimation Method ◽

Autonomous Driving ◽

Orientation Estimation

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Robust Visual Compass Using Hybrid Features for Indoor Environments

Electronics ◽

10.3390/electronics8020220 ◽

2019 ◽

Vol 8 (2) ◽

pp. 220 ◽

Cited By ~ 7

Author(s):

Ruibin Guo ◽

Keju Peng ◽

Dongxiang Zhou ◽

Yunhui Liu

Keyword(s):

Autonomous Navigation ◽

Estimation Method ◽

Rotation Matrix ◽

Indoor Environments ◽

3D Mapping ◽

Orientation Estimation ◽

Hybrid Features ◽

Depth Images ◽

Improved Accuracy ◽

Crucial Part

Orientation estimation is a crucial part of robotics tasks such as motion control, autonomous navigation, and 3D mapping. In this paper, we propose a robust visual-based method to estimate robots’ drift-free orientation with RGB-D cameras. First, we detect and track hybrid features (i.e., plane, line, and point) from color and depth images, which provides reliable constraints even in uncharacteristic environments with low texture or no consistent lines. Then, we construct a cost function based on these features and, by minimizing this function, we obtain the accurate rotation matrix of each captured frame with respect to its reference keyframe. Furthermore, we present a vanishing direction-estimation method to extract the Manhattan World (MW) axes; by aligning the current MW axes with the global MW axes, we refine the aforementioned rotation matrix of each keyframe and achieve drift-free orientation. Experiments on public RGB-D datasets demonstrate the robustness and accuracy of the proposed algorithm for orientation estimation. In addition, we have applied our proposed visual compass to pose estimation, and the evaluation on public sequences shows improved accuracy.

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