Audiovisual data fusion for successive speakers tracking

International audienceIn this paper, a human speaker tracking method on audio and video data is presented. It is applied to con- versation tracking with a robot. Audiovisual data fusion is performed in a two-steps process. Detection is performed independently on each modality: face detection based on skin color on video data and sound source localization based on the time delay of arrival on audio data. The results of those detection processes are then fused thanks to an adaptation of bayesian filter to detect the speaker. The robot is able to detect the face of the talking person and to detect a new speaker in a conversation

Multi-View Deep Learning for Consistent Semantic Mapping with RGB-D Cameras

Visual scene understanding is an important capability that enables robots to purposefully act in their environment. In this paper, we propose a novel approach to object-class segmentation from multiple RGB-D views using deep learning. We train a deep neural network to predict object-class semantics that is consistent from several view points in a semi-supervised way. At test time, the semantics predictions of our network can be fused more consistently in semantic keyframe maps than predictions of a network trained on individual views. We base our network architecture on a recent single-view deep learning approach to RGB and depth fusion for semantic object-class segmentation and enhance it with multi-scale loss minimization. We obtain the camera trajectory using RGB-D SLAM and warp the predictions of RGB-D images into ground-truth annotated frames in order to enforce multi-view consistency during training. At test time, predictions from multiple views are fused into keyframes. We propose and analyze several methods for enforcing multi-view consistency during training and testing. We evaluate the benefit of multi-view consistency training and demonstrate that pooling of deep features and fusion over multiple views outperforms single-view baselines on the NYUDv2 benchmark for semantic segmentation. Our end-to-end trained network achieves state-of-the-art performance on the NYUDv2 dataset in single-view segmentation as well as multi-view semantic fusion.Comment: the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017

Data fusion in ubiquitous networked robot systems for urban services

There is a clear trend in the use of robots to accomplish services that can help humans. In this paper, robots acting in urban environments are considered for the task of person guiding. Nowadays, it is common to have ubiquitous sensors integrated within the buildings, such as camera networks, and wireless communications like 3G or WiFi. Such infrastructure can be directly used by robotic platforms. The paper shows how combining the information from the robots and the sensors allows tracking failures to be overcome, by being more robust under occlusion, clutter, and lighting changes. The paper describes the algorithms for tracking with a set of fixed surveillance cameras and the algorithms for position tracking using the signal strength received by a wireless sensor network (WSN). Moreover, an algorithm to obtain estimations on the positions of people from cameras on board robots is described. The estimate from all these sources are then combined using a decentralized data fusion algorithm to provide an increase in performance. This scheme is scalable and can handle communication latencies and failures. We present results of the system operating in real time on a large outdoor environment, including 22 nonoverlapping cameras, WSN, and several robots.Universidad Pablo de Olavide. Departamento de Deporte e InformáticaPostprin

Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling

Long-term situation prediction plays a crucial role in the development of intelligent vehicles. A major challenge still to overcome is the prediction of complex downtown scenarios with multiple road users, e.g., pedestrians, bikes, and motor vehicles, interacting with each other. This contribution tackles this challenge by combining a Bayesian filtering technique for environment representation, and machine learning as long-term predictor. More specifically, a dynamic occupancy grid map is utilized as input to a deep convolutional neural network. This yields the advantage of using spatially distributed velocity estimates from a single time step for prediction, rather than a raw data sequence, alleviating common problems dealing with input time series of multiple sensors. Furthermore, convolutional neural networks have the inherent characteristic of using context information, enabling the implicit modeling of road user interaction. Pixel-wise balancing is applied in the loss function counteracting the extreme imbalance between static and dynamic cells. One of the major advantages is the unsupervised learning character due to fully automatic label generation. The presented algorithm is trained and evaluated on multiple hours of recorded sensor data and compared to Monte-Carlo simulation