The ego motion estimation from an image sequence, commonly known as visual odometry, has been thoroughly studied in recent years. Different solutions have been developed depending on the particular scenario the system interacts in. In highly textured environments point features are abundant and visual odometry approaches focus on complementary steps, such as sparse bundle adjustment or keyframe techniques, to improve the accuracy of the motion estimation. In textureless scenarios, the absence of point features motivates the use of different image features. Lines have proven to be an interesting alternative to points in man-made environments, but very few visual odometry approaches have been developed using these types of features. Moreover, the combination of point and line features has not been considered in the development of real-time visual odometry algorithms. In this paper, we explore the combination of point and line features to robustly compute the six degree of freedom motion transformation between consecutive stereo frames. Additionally, we deal with the problem of line stereo matching, since our approach is based on 3D-2D correspondences to estimate motion. We develop an efficient algorithm to compute the stereo line matching, even in situations where one of the endpoints describing the line segment in the left image is not visible in the right image. Several experiments with synthetic and real image sequences show that a simple but effective combination of point and line features improves the motion estimate compared to approaches using only one type of these features with a slight increase in computational cost.

One of the central problems in computer vision is the detection of semantically important objects and the estimation of their pose. Most of the work in object detection has been based on single image processing and its performance is limited by occlusions and ambiguity in appearance and geometry. This paper proposes an active approach to object detection by controlling the point of view of a mobile depth camera. When an initial static detection phase identifies an object of interest, several hypotheses are made about its class and orientation. The sensor then plans a sequence of viewpoints, which balances the amount of energy used to move with the chance of identifying the correct hypothesis. We formulate an active M-ary hypothesis testing problem, which includes sensor mobility, and solve it using a point-based approximate POMDP algorithm. The validity of our approach is verified through simulation and experiments with real scenes captured by a kinect sensor. The results suggest a significant improvement over static object detection.