Multisensor-based human detection and tracking for mobile service robots

The one of fundamental issues for service robots is human-robot interaction. In order to perform such a task and provide the desired services, these robots need to detect and track people in the surroundings. In the present paper, we propose a solution for human tracking with a mobile robot that implements multisensor data fusion techniques. The system utilizes a new algorithm for laser-based legs detection using the on-board LRF. The approach is based on the recognition of typical leg patterns extracted from laser scans, which are shown to be very discriminative also in cluttered environments. These patterns can be used to localize both static and walking persons, even when the robot moves. Furthermore, faces are detected using the robot's camera and the information is fused to the legs position using a sequential implementation of Unscented Kalman Filter. The proposed solution is feasible for service robots with a similar device configuration and has been successfully implemented on two different mobile platforms. Several experiments illustrate the effectiveness of our approach, showing that robust human tracking can be performed within complex indoor environments

Robot control based on qualitative representation of human trajectories

A major challenge for future social robots is the high-level interpretation of human motion, and the consequent generation of appropriate robot actions. This paper describes some fundamental steps towards the real-time implementation of a system that allows a mobile robot to transform quantitative information about human trajectories (i.e. coordinates and speed) into qualitative concepts, and from these to generate appropriate control commands. The problem is formulated using a simple version of qualitative trajectory calculus, then solved using an inference engine based on fuzzy temporal logic and situation graph trees. Preliminary results are discussed and future directions of the current research are drawn

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

Spatial context-aware person-following for a domestic robot

Domestic robots are in the focus of research in terms of service providers in households and even as robotic companion that share the living space with humans. A major capability of mobile domestic robots that is joint exploration of space. One challenge to deal with this task is how could we let the robots move in space in reasonable, socially acceptable ways so that it will support interaction and communication as a part of the joint exploration. As a step towards this challenge, we have developed a context-aware following behav- ior considering these social aspects and applied these together with a multi-modal person-tracking method to switch between three basic following approaches, namely direction-following, path-following and parallel-following. These are derived from the observation of human-human following schemes and are activated depending on the current spatial context (e.g. free space) and the relative position of the interacting human. A combination of the elementary behaviors is performed in real time with our mobile robot in different environments. First experimental results are provided to demonstrate the practicability of the proposed approach