Depth sensors have become increasingly popular in interactive computer vision applications. Currently, most of these applications are limited to indoor use. Popular IR-based depth sensors cannot provide depth data when exposed to sunlight. In these cases, one can still obtain depth information using a stereo camera set up or a special outdoor Time-of-Flight camera, at the cost of a reduced quality of the depth image. The resulting depth images are often incomplete and suffer from low resolution, noise and missing information. The aim of this paper is to recover the missing depth information based on an extension of SEEDS superpixels [11]. The superpixel segmentation algorithm is extended to take depth information into account where available. The approach takes advantage of the boundary-updating property of SEEDS. The result is a clean segmentation that recovers the missing depth information in a low-quality depth image. We test the approach outdoors on an interactive urban robot. The system is used to segment a person in front of the robot, and to detect body parts for interaction with the robot using pointing gestures.

This video presents a motion planning method enabling an autonomous mobile robot to approach a person to initiate a conversation proactively. The proposed concept implements social aspects to give motions a more human-like appearance. This is intended since experiments discussed in literature have shown that it is easier for people to predict and read the purpose of human-like movements. User study results show that the readability of the approach behavior is improved by including parameters such as path execution speed, distance between goal pose and person, position and orientation in front of the person, and trajectory shape. The video depicts the results of an implemented human approach planner for dynamic environments. It plans a trajectory towards a person applying the named set of social constraints. Simulations and real world experiments show how the approaching behavior smoothly brings the robot close to a person for interaction.

In this paper we propose a novel approach to detect and reconstruct transparent objects. This approach makes use of the fact that many transparent objects, especially the ones consisting of usual glass, absorb light in certain wavelengths [1]. Given a controlled illumination, this absorption is measurable in the intensity response by comparison to the background. We show the usage of a standard infrared emitter and the intensity sensor of a time of flight (ToF) camera to reconstruct the structure given we have a second view point. The structure can not be measured by the usual 3D measurements of the ToF camera. We take advantage of this fact by deriving this internal sensory contradiction from two ToF images and reconstruct an approximated surface of the original transparent object. Therefor we are using a perspectively invariant matching in the intensity channels from the first to the second view of initially acquired candidates. For each matched pixel in the first view a 3D movement can be predicted given their original 3D measurement and the known distance to the second camera position. If their line of sight did not pass a transparent object or suffered any other major defect, this prediction will highly correspond to the actual measured 3D points of the second view. Otherwise, if a detectable error occurs, we approximate a more exact point to point matching and reconstruct the original shape by triangulating the points in the stereo setup. We tested our approach using a mobile platform with one Swissranger SR4k. As this platform is mobile, we were able to create a stereo setup by moving it. Our results show a detection of transparent objects on tables while simultaneously identifying opaque objects that also existed in the test setup. The viability of our results is demonstrated by a successful automated manipulation of the respective transparent object.

This paper implements a real-time hand gesture recognition algorithm based on the inexpensive Kinect sensor. The use of a depth sensor allows for complex 3D gestures where the system is robust to disturbing objects or persons in the background. A Haarlet-based hand gesture recognition system is implemented to detect hand gestures in any orientation, and more in particular pointing gestures while extracting the 3D pointing direction. The system is integrated on an interactive robot (based on ROS), allowing for real-time hand gesture interaction with the robot. Pointing gestures are translated into goals for the robot, telling him where to go. A demo scenario is presented where the robot looks for persons to interact with, asks for directions, and then detects a 3D pointing direction. The robot then explores his vicinity in the given direction and looks for a new person to interact with.