Indoor Obstacle Discovery on Reflective Ground via Monocular Camera
Feng Xue Yicong Chang Tianxi Wang Yu Zhou Anlong Ming
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.