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Socially interacting robots will need to understand the intentions and recognize the behaviors of people they come in contact with. In this paper we look at how a robot can learn to recognize and predict people's intended path based on its own observations of people over time. Our approach uses people tracking on the robot from either RGBD cameras or LIDAR. The tracks are separated into homogeneous...
Long-term autonomous learning of human environments entails modelling and generalizing over distinct variations in: object instances in different scenes, and different scenes with respect to space and time. It is crucial for the robot to recognize the structure and context in spatial arrangements and exploit these to learn models which capture the essence of these distinct variations. Table-tops posses...
Many robot perception systems are built to only consider intrinsic object features to recognise the class of an object. By integrating both top-down spatial relational reasoning and bottom-up object class recognition the overall performance of a perception system can be improved. In this paper we present a unified framework that combines a 3D object class recognition system with learned, spatial models...
We present a novel method for re-creating the static structure of cluttered office environments - which we define as the “meta-room” - from multiple observations collected by an autonomous robot equipped with an RGB-D depth camera over extended periods of time. Our method works directly with point clusters by identifying what has changed from one observation to the next, removing the dynamic elements...
This paper presents a novel approach to model motion patterns of dynamic objects, such as people and vehicles, in the environment with the occupancy grid map representation. Corresponding to the ever-changing nature of the motion pattern of dynamic objects, we model each occupancy grid cell by an IOHMM, which is an inhomogeneous variant of the HMM. This distinguishes our work from existing methods...
Finding accurate correspondences between overlapping 3D views is crucial for many robotic applications, from multi-view 3D object recognition to SLAM. This step, often referred to as view registration, plays a key role in determining the overall system performance. In this paper, we propose a fast and simple method for registering RGB-D data, building on the principle of the Iterative Closest Point...
<?Pub Dtl?>This paper describes a system for reacquiring features of interest in a shallow-water ocean environment, using autonomous underwater vehicles (AUVs) equipped with low-cost sonar and navigation sensors. In performing mine countermeasures, it is critical to enable AUVs to navigate accurately to previously mapped objects of interest in the water column or on the seabed, for further assessment...
Many robotics tasks require the robot to predict what lies in the unexplored part of the environment. Although much work focuses on building autonomous robots that operate indoors, indoor environments are neither well understood nor analyzed enough in the literature. In this paper, we propose and compare two methods for predicting both the topology and the categories of rooms given a partial map....
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