We distributed fourteen controllable street lamps in a city square and recorded three comparative and one 'usual' condition, operating the public lighting as if it were an interactive stage. First tested was adaptive lighting that responded to people's occupancy patterns. Second was a mobile phone application that allowed people to customise color and responsive behaviours in the overhead lighting system. Third was ambient lighting, responding to wind velocity. The study extends the discussion on multiuser interaction design in public lighting by asking: how can interactions using mobile phones, thermal tracking and wind inputs afford new social behaviors, without disturbing the usual public functions of street lighting? This research lays foundational work on the affordances of mobile phones for engagement and interaction with public lighting. The study indicates the use of personal phones as a tool for interaction in this setting has potential to provide a stronger ownership to urban place.

This paper presents a full-scale experiment investigating the use of human motion intensities as input for interactive illumination of a town square in the city of Aalborg in Denmark. As illuminators sixteen 3.5 meter high RGB LED lamps were used. The activity on the square was monitored by three thermal cameras and analysed by computer vision software from which motion intensity maps and peoples trajectories were estimated and used as input to control the interactive illumination. The paper introduces a 2-layered interactive light strategy addressing ambient and effect illumination criteria totally four light scenarios were designed and tested. The result shows that in general people immersed in the street lighting did not notice that the light changed according to their presence or actions, but people watching from the edge of the square noticed the interaction between the illumination and the immersed persons. The experiment also demonstrated that interactive can give significant power savings. In the current experiment there was a difference of 92% between the most and less energy consuming light scenario.

Sampling based techniques for robot motion planning have become more widespread during the last decade. The algorithms however, still struggle with for example narrow passages in the configuration space and suffer from high number of necessary samples, especially in higher dimensions. A widely used method is Rapidly-exploring Random Trees (RRT's). One problem with this method is the nearest neighbour search time, which grows significantly when adding a large number of vertices. We propose an algorithm which decreases the computation time, such that more vertices can be added in the same amount of time to generate better trajectories. The algorithm is based on subdividing the configuration space into boxes, where only specific boxes needs to be searched to find the nearest neighbour. It is shown that the computational complexity is lowered from a theoretical point of view. The result is an algorithm that can provide better trajectories within a given time period, or alternatively compute trajectories faster. In simulation the algorithm is verified for a simple RRT implementation and in a more specific case where a robot has to plan a path through a human inhabited environment.

Robots for elderly have drawn a great deal of attention as it is a controversial topic being pushed forward by the fact that there will be a dramatic increase of elderly in most western countries. Within the field of HRI, much research has been conducted on robots interacting with elderly and also a number of commercial products have been introduced to the market. Since 2006, a number of projects have been launched in Denmark in order to evaluate robot technology in practice in elder care. This paper gives an brief overview of a selected number of projects and outlines the characteristics and results. Finally it is discussed how HRI can benefit from these.

This paper presents a trajectory planning algorithm for a robot operating in dynamic human environments. Environments such as pedestrian streets, hospital corridors, train stations or airports. We formulate the problem as planning a minimal cost trajectory through a potential field, defined from the perceived position and motion of persons in the environment. A Rapidly-exploring Random Tree (RRT) algorithm is proposed as a solution to the planning problem, and a new method for selecting the best trajectory in the RRT, according to the cost of traversing a potential field, is presented. The RRT expansion is enhanced to account for the kinodynamic robot constraints by using a robot motion model and a controller to add a reachable vertex to the tree. Instead of executing a whole trajectory, when planned, the algorithm uses a Model Predictive Control (MPC) approach, where only a short segment of the trajectory is executed while a new iteration of the RRT is computed. The planning algorithm is demonstrated in a simulated pedestrian street environment.

Applications with street navigation have been recently introduced on mobile phone devices. A major part of existing systems use integrated GPS as input for indicating the location. However, these systems often fail or make abrupt shifts in urban environment due to occlusion of satellites. Furthermore, they only give the position of a person and not the object of his attention, which is just as important for localization based services. In this paper we introduce a system using mobile phones built-in cameras for navigation and localization using visual information in accordance with the way we as humans navigate. The introduced method uses local features for extraction of natural feature points from images which are compared to a database for localization. The system is tested and evaluated in a real urban environment and the result shows very high success rate.

The focus is to study people's willingness to interact, and their reaction when a robot enters their daily environment. The robot autonomously detects and follows people, while keeping a safe distance. The conclusion was that people were positive towards robots in their daily life. The experiments gave knowledge about using robots in open-ended environments, and knowledge about people's reactions and expectations to robots in urban environments.

This paper introduces a new method to determine a person's pose based on laser range measurements. Such estimates are typically a prerequisite for any human-aware robot navigation, which is the basis for effective and time extended interaction between a mobile robot and a human. The robot uses observed information from a laser range finder to detect persons and their position relative to the robot. This information together with the motion of the robot itself is fed through a Kalman filter, which utilizes a model of the human kinematic movement to produce an estimate of the person's pose. The resulting pose estimates are used to identify humans who wish to be approached and interacted with. The behaviour of the robot is based on adaptive potential functions adjusted accordingly such that the persons social spaces are respected. The method is tested in experiments that demonstrate the potential of the combined pose estimation and adaptive behaviour approach.

Interest points have been used as local features with success in many computer vision applications such as image/video retrieval and object recognition. However, a major issue when using this approach is a large number of interest points detected from each image and created a dense feature space. This influences the processing speed in any runtime application. Selecting the most important features to reduce the size of the feature space will solve this problem. Thereby this raises a question of what makes a feature more important than the others? In this paper, we present a new technique to choose a subset of features. Our approach differs from others in a fact that selected feature is based on the context of the given image. Our experimental results show a significant reduction rate of features while preserving the retrieval performance.

Respecting people's social spaces is an important prerequisite for acceptable and natural robot navigation in human environments. In this paper, we describe an adaptive system for mobile robot navigation based on estimates of whether a person seeks to interact with the robot or not. The estimates are based on run-time motion pattern analysis compared to stored experience in a database. Using a potential field centered around the person, the robot positions itself at the most appropriate place relative to the person and the interaction status. The system is validated through qualitative tests in a real world setting. The results demonstrate that the system is able to learn to navigate based on past interaction experiences, and to adapt to different behaviors over time.

In literature, existing researches on building recognition mainly concentrate on scales, rotations, and viewpoints variance. In urban environment, large temporal variations of weather and lighting conditions should also be considered as major challenges for robust recognition. For instances, there are differences between images captured during daytime and nighttime, especially significant changes in building appearances between seasons because of the differences in light setting. To date, these large temporal variation issues have not been fully investigated. In this paper, we therefore focus on constructing a system that deals with the temporal difference factors in recognizing urban buildings. In order to build such a system, two main criteria are raised, namely the efficiency of the recognition algorithm and the speed for interactive search purpose. For recognition purpose, we exploit the MOPS features (Multi-scale Oriented Patches) in [2], which extract features of patches around interest points. To speed up the searching process, we employ the vocabulary tree based search technique in [12]. Our final system shows high performance in recognizing buildings under significant temporal variations with a fast processing reaction.

A method for capturing high intensity dynamic range scenes with a low dynamic range camera consists in taking a series of images with different exposure settings and combining these into a single high dynamic range image. The combined image values are found by weighted averaging of values from the differently exposed images on a per-pixel basis. This paper reviews existing weighting schemes and considers their noise properties. Furthermore, a minimum-variance solution is introduced which exploits a camera noise model. Special emphasis is on the case when the camera is linear. A method is given for estimating the uncertainty of the combined image values. The results are validated experimentally.

Colour is an important and useful feature for object tracking and recognition in computer vision. However, it has the difficulty that the colour of the object changes if the illuminant colour changes. But under known illuminant colour it becomes a robust feature. There are more and more computer vision applications tracking humans, for example in interfaces for human computer interaction or automatic camera men, where skin colour is an often-used feature. Hence, it would be of significant importance to know the illuminant colour in such applications. This paper proposes a novel method to estimate the current illuminant colour from skin colour observations. The method is based on a physical model of reflections, the assumption that illuminant colours are located close to the Planckian locus, and the knowledge about the camera parameters. The method is empirically tested using real images. The average estimation error of the correlated colour temperature is as small as 180 K. Applications are for example in colour-based tracking to adapt to changes in lighting and in visualisation to re-render image colours to their appearance under canonical viewing conditions.