Road Surface Classification with Images Captured From Low-cost Camera - Road Traversing Knowledge (RTK) Dataset

The type of road pavement directly influences the way vehicles are driven. It’s common to find papers that deal with path detection but don’t take into account major changes in road surface patterns. The quality of the road surface has a direct impact on the comfort and especially on the safety of road users. In emerging countries it’s common to find unpaved roads or roads with no maintenance. Unpaved or damaged roads also impact in higher fuel costs and vehicle maintenance. This kind of analysis can be useful for both road maintenance departments as well as for autonomous vehicle navigation systems to verify potential critical points. For the experiments accomplishment upon the surface types and quality classification, we present a new dataset, collected with a low-cost camera. This dataset has examples of good and bad asphalt (with potholes and other damages) other types of pavement and also many examples of unpaved roads (with and without potholes). We also provide several frames from our dataset manually sorted in surface types for tests accuracy verification. Our road type and quality classifier was done through a simple Convolutional Neural Network with few steps and presents promising results in different datasets

Comparison between low-cost passive and active vision for obstacle depth

Obstacle detection is a key issue in many current applications, especially in applications that have been increasingly highlighted such as: advanced driver assistance systems (ADAS), simultaneous localization and mapping (SLAM) and autonomous navigation system. This can be achieved by active and passive acquisition vision systems, for example: laser and cameras respectively. In this paper we present a comparison between low-cost active and passive devices, more specifically LIDAR and two cameras. To this comparison a disparity map is created by stereo correspondence through two images and a point cloud map created by LIDAR data values (distances measures). The obtained results shown that passive vision can be as good as or even better than active vision in low cost scenarios

Comparison between low-cost passive and active vision for obstacle depth

Obstacle detection is a key issue in many current applications, especially in applications that have been increasingly highlighted such as: advanced driver assistance systems (ADAS), simultaneous localization and mapping (SLAM) and autonomous navigation system. This can be achieved by active and passive acquisition vision systems, for example: laser and cameras respectively. In this paper we present a comparison between low-cost active and passive devices, more specifically LIDAR and two cameras. To this comparison a disparity map is created by stereo correspondence through two images and a point cloud map created by LIDAR data values (distances measures). The obtained results shown that passive vision can be as good as or even better than active vision in low cost scenarios

Comparison between low-cost passive and active vision for obstacle depth

Obstacle detection is a key issue in many current applications, especially in applications that have been increasingly highlighted such as: advanced driver assistance systems (ADAS), simultaneous localization and mapping (SLAM) and autonomous navigation system. This can be achieved by active and passive acquisition vision systems, for example: laser and cameras respectively. In this paper we present a comparison between low-cost active and passive devices, more specifically LIDAR and two cameras. To this comparison a disparity map is created by stereo correspondence through two images and a point cloud map created by LIDAR data values (distances measures). The obtained results shown that passive vision can be as good as or even better than active vision in low cost scenarios