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1 Problem Statement and Related Work RGB-D cameras are novel sensing systems that capture RGB images along with per-pixel depth information. RGB-D cameras rely on either structured light patterns combined with stereo sensing [6, 10] or time-of-flight laser sensing [1] to generate depth estimates that can be associated with RGB pixels. Very soon, small,(More)
RGB-D cameras provide both a color image and per-pixel depth estimates. The richness of their data and the recent development of low-cost sensors have combined to present an attractive opportunity for mobile robotics research. In this paper, we describe a system for visual odometry and mapping using an RGB-D camera, and its application to autonomous flight.(More)
Financial markets appear to improve the allocation of capital – across 65 countries, those with developed financial markets increase investment more in growing industries, and decrease investment more in declining industries, than financially undeveloped countries. The efficiency of capital allocation is also negatively correlated with the extent of state(More)
RGB-D cameras (such as the Microsoft Kinect) are novel sensing systems that capture RGB images along with per-pixel depth information. In this paper we investigate how such cameras can be used for building dense 3D maps of indoor environments. Such maps have applications in robot navigation, manipulation, semantic mapping, and telepresence. We present RGB-D(More)
We show that equity market liberalizations, on average, lead to a one percent increase in annual real economic growth over a five-year period. The effect is robust to alternative definitions of liberalization and does not reflect variation in the world business cycle. The effect also remains intact when an exogenous measure of growth opportunities is(More)
for helpful advice concerning the planning and implementation of empirical research described here. We are also grateful to and Jojanneke van der Toorn for assistance at various stages with data collection, entry, or analysis. Finally, we credit Rod Kramer and Barry Staw with improving the chapter by providing characteristically insightful and constructive(More)
The goal of this research is to enable mobile robots to navigate through crowded environments such as indoor shopping malls, airports, or downtown side walks. The key research question addressed in this paper is how to learn planners that generate human-like motion behavior. Our approach uses inverse reinforcement learning (IRL) to learn human-like(More)
RGB-D cameras provide both color images and per-pixel depth estimates. The richness of this data and the recent development of low-cost sensors have combined to present an attractive opportunity for mobile robotics research. In this paper, we describe a system for visual odometry and mapping using an RGB-D camera, and its application to autonomous flight.(More)
Detailed 3D visual models of indoor spaces, from walls and floors to objects and their configurations, can provide extensive knowledge about the environments as well as rich contextual information of people living therein. Vision-based 3D modeling has only seen limited success in applications, as it faces many technical challenges that only a few experts(More)
Recent advances have allowed for the creation of dense, accurate 3D maps of indoor environments using RGB-D cameras. Some techniques are able to create large-scale maps, while others focus on accurate details using GPU-accelerated volumetric representations. In this work we describe patch volumes, a novel multiple-volume representation which enables the(More)