Visual obstacle discovery is a key step towards autonomous navigation of indoor mobile robots. Successful solutions have many applications in multiple scenes. One of the exceptions is the reflective ground. In this case, the reflections on the floor resemble the true world, which confuses the obstacle discovery and leaves navigation unsuccessful. We argue that the key to this problem lies in obtaining discriminative features for reflections and obstacles. Note that obstacle and reflection can be separated by the ground plane in 3D space. With this observation, we firstly introduce a pre-calibration based ground detection scheme that uses robot motion to predict the ground plane. Due to the immunity of robot motion to reflection, this scheme avoids failed ground detection caused by reflection. Given the detected ground, we design a ground-pixel parallax to describe the location of a pixel relative to the ground. Based on this, a unified appearance-geometry feature representation is proposed to describe objects inside rectangular boxes. Eventually, based on segmenting by detection framework, an appearance-geometry fusion regressor is designed to utilize the proposed feature to discover the obstacles. It also prevents our model from concentrating too much on parts of obstacles instead of whole obstacles. For evaluation, we introduce a new dataset for Obstacle on Reflective Ground (ORG), which comprises 15 scenes with various ground reflections, a total of more than 200 image sequences and 3400 RGB images. The pixel-wise annotations of ground and obstacle provide a comparison to our method and other methods. By reducing the misdetection of the reflection, the proposed approach outperforms others. The source code and the dataset will be available at https://github.com/XuefengBUPT/IndoorObstacleDiscovery-RG.

翻译：视觉障碍物发现是实现室内移动机器人自主导航的关键步骤。成功的解决方案在多种场景中具有广泛应用，但反光地面是一个例外。在此情况下，地面上的反射会与真实世界混淆，导致障碍物发现出现偏差，使导航失败。我们认为解决这一问题的关键在于获取反射与障碍物之间的判别性特征。值得注意的是，在三维空间中，障碍物与反射可通过地平面分离。基于此观察，我们首先引入一种基于预校准的地面检测方案，利用机器人运动预测地平面。由于机器人运动对反射具有抗干扰性，该方案避免了由反射导致的地面检测失败。在检测到地面后，我们设计了"地面像素视差"来描述像素相对于地面的位置。基于此，提出了一种统一的外观-几何特征表示来描述矩形框内的物体。最终，基于检测框架的分割思想，设计了外观-几何融合回归器，利用所提出的特征发现障碍物。该回归器还防止模型过度关注障碍物局部而非整体。为进行评估，我们提出了反光地面障碍物（ORG）数据集，包含15个具有不同地面反射的场景，总计超过200个图像序列和3400张RGB图像。地面与障碍物的像素级标注为我们的方法与其他方法提供了对比。通过减少对反射的误检，所提方法表现优于其他方法。源代码与数据集将于https://github.com/XuefengBUPT/IndoorObstacleDiscovery-RG 开放。