Color-depth cameras (RGB-D cameras) have become the primary sensors in most robotics systems, from service robotics to industrial robotics applications. Typical consumer-grade RGB-D cameras are provided with a coarse intrinsic and extrinsic calibration that generally does not meet the accuracy requirements needed by many robotics applications [e.g., highly accurate three-dimensional (3-D) environment reconstruction and mapping, high precision object recognition, localization, etc.]. In this paper, we propose a human-friendly, reliable, and accurate calibration framework that enables to easily estimate both the intrinsic and extrinsic parameters of a general color-depth sensor couple. Our approach is based on a novel two components error model. This model unifies the error sources of RGB-D pairs based on different technologies, such as structured-light 3-D cameras and time-of-flight cameras. Our method provides some important advantages compared to other state-of-the-art systems: It is general (i.e., well suited for different types of sensors), based on an easy and stable calibration protocol, provides a greater calibration accuracy, and has been implemented within the robot operating system robotics framework. We report detailed experimental validations and performance comparisons to support our statements.

The availability of affordable depth sensors in conjunction with common RGB cameras (even in the same device, e.g. the Microsoft Kinect) provides robots with a complete and instantaneous representation of both the appearance and the 3D structure of the current surrounding environment. This type of information enables robots to safely navigate, perceive and actively interact with other agents inside the working environment. It is clear that, in order to obtain a reliable and accurate representation, not only the intrinsic parameters of each sensors should be precisely calibrated, but also the extrinsic parameters relating the two sensors should be precisely known. In this paper, we propose a human-friendly and reliable calibration framework, that enables to easily estimate both the intrinsic and extrinsic parameters of a camera-depth sensor couple. Real world experiments using a Kinect show improvements for both the 3D structure estimation and the association tasks.

This paper addresses recommending presentation sessions at smart conferences to participants. We propose a venue recommendation algorithm: socially aware recommendation of venues and environments (SARVE). SARVE computes correlation and social characteristic information of conference participants. In order to model a recommendation process using distributed community detection, SARVE further integrates the current context of both the smart conference community and participants. SARVE recommends presentation sessions that may be of high interest to each participant. We evaluate SARVE using a real-world dataset. In our experiments, we compare SARVE with two related state-of-the-art methods, namely context-aware mobile recommendation services and conference navigator (recommender) model. Our experimental results show that in terms of the utilized evaluation metrics, i.e., precision, recall, and f-measure, SARVE achieves more reliable and favorable social (relations and context) recommendation results.

Combining evaluations of different kinds, like of user feedback origin, is often a necessary optimization step in communications software and in several applied areas that require personalized or statistical customization. This paper is introducing a fingerprinting fusion approach to identify the optimal operator inside a family of well known and identified as suitable information retrieval data-fusion methods; even if the approach was identified to address an issue related to the identification of the optimal streaming server for differential positional data from satellite systems, the model presented and the methodology followed define a multidisciplinary approach that can be utilized in several experimental areas utilizing information retrieval data-fusion methods. A mobile prototype was developed and simulation data was utilized to experiment and evaluate the proposal, with good results, identifying that for the specific representation a linear operator that outperforms weighted CombSUM and CombMAX.

This paper proposes a very fast and robust multi-people tracking algorithm suitable for mobile platforms equipped with a RGB-D sensor. Our approach features a novel depth-based sub-clustering method explicitly designed for detecting people within groups or near the background and a three-term joint likelihood for limiting drifts and ID switches. Moreover, an online learned appearance classifier is proposed, that robustly specializes on a track while using the other detections as negative examples. Tests have been performed with data acquired from a mobile robot in indoor environments and on a publicly available dataset acquired with three RGB-D sensors and results have been evaluated with the CLEAR MOT metrics. Our method reaches near state of the art performance and very high frame rates in our distributed ROS-based CPU implementation.