Abstract — Wheeled tour guide robots have already been deployed in various museums or fairs worldwide. A key requirement for successful tour guide robots is to interact with people and to entertain them. Most of the previous tour guide robots, however, focused more on the involved navigation task than on natural interaction with humans. Humanoid robots, on the other hand, offer a great potential for investigating intuitive, multimodal interaction between humans and machines. In this paper, we present our mobile full-body humanoid tour guide robot Robotinho. We provide mechanical and electrical details and cover perception, the integration of multiple modalities for interaction, navigation control, and system integration aspects. The multimodal interaction capabilities of Robotinho have been designed and enhanced according to the questionnaires filled out by the people who interacted with the robot at previous public demonstrations. We present experiences we have made during experiments in which untrained users interacted with the robot. I.

Abstract — In this paper, we present a localization method for humanoid robots navigating in arbitrary complex indoor environments using only onboard sensing. Reliable and accurate localization for humanoid robots operating in such environments is a challenging task. First, humanoids typically execute motion commands rather inaccurately and odometry can be estimated only very roughly. Second, the observations of the small and lightweight sensors of most humanoids are seriously affected by noise. Third, since most humanoids walk with a swaying motion and can freely move in the environment, e.g., they are not forced to walk on flat ground only, a 6D torso pose has to be estimated. We apply Monte Carlo localization to globally determine and track a humanoid’s 6D pose in a 3D world model, which may contain multiple levels connected by staircases. To achieve a robust localization while walking and climbing stairs, we integrate 2D laser range measurements as well as attitude data and information from the joint encoders. We present simulated as well as real-world experiments with our humanoid and thoroughly evaluate our approach. As the experiments illustrate, the robot is able to globally localize itself and accurately track its 6D pose over time. I.

Abstract—In the past, there has been a tremendous advance in the area of simultaneous localization and mapping (SLAM). However, there are relatively few approaches for incorporating prior information or knowledge about structural similarities into the mapping process. Consider, for example, office buildings in which most of the offices have an identical geometric layout. The same typically holds for the individual stories of buildings. In this paper, we propose an approach for generating alignment constraints between different floors of the same building in the context of graph-based SLAM. This is done under the assumption that the individual floors of a building share at least some structural properties. To identify such areas, we apply a particle filter-based localization approach using maps and observations from different floors. We evaluate our system using several real datasets as well as in simulation. The results demonstrate that our approach is able to correctly align multiple floors and allows the robot to generate consistent models of multi-story buildings. I.

Abstract — In this paper, we present an approach to obstacle detection for collision-free, efficient humanoid robot navigation based on monocular images and sparse laser range data. To detect arbitrary obstacles in the surroundings of the robot, we analyze 3D data points obtained from a 2D laser range finder installed in the robot’s head. Relying only on this laser data, however, can be problematic. While walking, the floor close to the robot’s feet is not observable by the laser sensor, which inherently increases the risk of collisions, especially in nonstatic scenes. Furthermore, it is time-consuming to frequently stop walking and tilting the head to obtain reliable information about close obstacles. We therefore present a technique to train obstacle detectors for images obtained from a monocular camera also located in the robot’s head. The training is done online based on sparse laser data in a self-supervised fashion. Our approach projects the obstacles identified from the laser data into the camera image and learns a classifier that considers color and texture information. While the robot is walking, it then applies the learned classifiers to the images to decide which areas are traversable. As we illustrate in experiments with a real humanoid, our approach enables the robot to reliably avoid obstacles during navigation. Furthermore, the results show that our technique leads to significantly more efficient navigation compared to extracting obstacles solely based on 3D laser range data acquired while the robot is standing at certain intervals. I.