At present, the vehicle obstacle detection system usually uses different devices or sensors to perceive and obtain the obstacle information. However, omni-directional obstacle detection is difficult to realize because these devices or sensors are usually easy to be affected by environmental lighting and the material properties of the obstacle surface. Furthermore, most sensors have limited information regarding distance, which limits their application to omni-directional obstacle detection. To solve this problem, this paper proposes a method using depth camera for omni-directional obstacle detection. A method applying region growth for depth image and a fast inpainting method for depth image are proposed to extract and repair the obstacle regions in the depth images obtained by installing depth cameras around the car body. An improved method applying iterative normalized cut is also proposed to cluster and segment fragmentary and irregular obstacle regions to generate the complete obstacle regions. Finally, the obstacle regions are overviewed using a three-dimensional visualization method to realize omni-directional obstacle viewing. The results of experiments conducted in an environment with different obstacles during the day and night demonstrate that, compared with other methods, our proposed approach can effectively promote the ability to detect complex obstacles, and largely improve the detection speed; furthermore, obstacle detection using our method will be unaffected by environmental lighting. Each of these advantages provided by our method can significantly promote the driving safety of unmanned or other types of vehicles.

中文翻译：

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多种测量系统多辐射场电荷比较方法的优化


目前，车辆障碍物检测系统通常采用不同的器件或传感器来感知并获取障碍物信息。然而，全向障碍物检测很难实现，因为这些设备或传感器通常容易受到环境光照和障碍物表面材料特性的影响。此外，大多数传感器有关距离的信息有限，这限制了它们在全向障碍物检测中的应用。针对这一问题，本文提出一种利用深度相机进行全方位障碍物检测的方法。提出了一种应用深度图像区域生长的方法和一种深度图像快速修复方法，以提取和修复在车身周围安装深度相机获得的深度图像中的障碍物区域。还提出了一种应用迭代归一化切割的改进方法，对碎片和不规则障碍物区域进行聚类和分割，以生成完整的障碍物区域。最后，利用三维可视化方法对障碍物区域进行概览，实现全方位障碍物查看。在白天和夜间不同障碍物的环境下进行的实验结果表明，与其他方法相比，我们提出的方法可以有效提升复杂障碍物的检测能力，并大大提高检测速度；此外，使用我们的方法进行障碍物检测将不受环境照明的影响。我们的方法提供的每一个优点都可以显着提高无人驾驶或其他类型车辆的驾驶安全性。