Object segmentation and classification is an important and difficult task in robotic vision. The task is complicated even further when the different objects are partially or completely occluded. Allowing a robot to take measurements from varying points of view can help in alleviating or completely removing occlusions. A robot equipped with an RGB-D sensor has the capability of searching for new and better points of view to facilitate object recognition. In this work, a motion control algorithm is designed and implemented on a mobile robot to facilitate object classification in RGB-D data of clustered objects.

In this paper, we introduce a new covariance based feature descriptor to be used on “colored” point clouds gathered by a mobile robot equipped with an RGB-D camera. Although many recent descriptors provide adequate results, there is not yet a clear consensus on how to best tackle “colored” point clouds. We present the notion of a covariance on RGB-D data. Covariances have not only been proven to be successful in image processing, but in other domains as well. Their main advantage is that they provide a compact and flexible description of point clouds. Our work is a first step towards demonstrating the usability of the concept of covariances in conjunction with RGB-D data. Experiments performed on an RGB-D database and compared to previous results show the increased performance of our method.

One of the most important tasks for mobile robots is to sense their environment. Further tasks might include the recognition of objects in the surrounding environment. Three dimensional range finders have become the sensors of choice for mapping the environment of a robot. Recognizing objects in point clouds provided by such sensors is a difficult task. The main contribution of this paper is the introduction of a new covariance based point cloud descriptor for such object recognition. Covariance based descriptors have been very successful in image processing. One of the main advantages of these descriptors is their relatively small size. The comparisons between different covariance matrices can also be made very efficient. Experiments with real world and synthetic data will show the superior performance of the covariance descriptors on point clouds compared to state-of-the-art methods.

Cameras are becoming a common tool for automated vision purposes due to their low cost. In an era of growing security concerns, camera surveillance systems have become not only important but also necessary. Algorithms for several tasks such as detecting abandoned objects and tracking people have already been successfully developed. While tracking people is relatively easy, counting people in groups is much more challenging. The mutual occlusions between people in a group make it difficult to provide an exact count. The aim of this work is to present a method of estimating the number of people in group scenarios. Several considerations for counting people are illustrated in this paper, and experimental results of the method are described and discussed.

Cameras are becoming a common tool for automated vision purposes due to their low cost. Many surveillance and inspection systems include cameras as their sensor of choice. How useful these camera systems are is very dependent upon the positioning of the cameras. This is especially true if the cameras are to be used in automated systems as a beneficial camera placement will simplify image processing operations. Therefore, a reliable positioning algorithm can lower the processing requirements of the system. In this paper several considerations for improving camera placement are investigated with the goal of developing a general algorithm that can be applied to a variety of systems. This paper presents this algorithm for placement problem in the context of computer vision and robotics. Simulated results of our method are then shown and discussed, along with an outline of future work.

The desire for a high mobility-to-size ratio in mobile robots has led to the exploration of many new methods of locomotion, one of which is tumbling. To the authors' knowledge, there are very few tumbling robots in existence and no formalized methods for their control. In this paper we begin addressing these issues by presenting an approach for deriving motion primitives for tumbling robots. We apply our method to the specific case of a two-armed tumbling robot and include the final derived motion primitives. Additionally we discuss in general the motion of tumbling robots and introduce 3 useful gaits derived from the resulting primitives of our approach.