Abstract

Abstract. We propose a framework for automatic modeling, detection, and tracking of 3D objects with a Kinect. The detection part is mainly based on the recent template-based LINEMOD approach [1] for object detection. We show how to build the templates automatically from 3D models, and how to estimate the 6 degrees-of-freedom pose accurately and in real-time. The pose estimation and the color information allow us to check the detection hypotheses and improves the correct detection rate by 13% with respect to the original LINEMOD. These many improvements make our framework suitable for object manipulation in Robotics applications. Moreover we propose a new dataset made of 15 registered, 1100+ frame video sequences of 15 various objects for the evaluation of future competing methods. Fig. 1. 15 different texture-less 3D objects are simultaneously detected with our approach under different poses on heavy cluttered background with partial occlusion. Each detected object is augmented with its 3D model. We also show the corresponding coordinate systems.

This article describes the robot Stanley, which won the 2005 DARPA Grand Challenge. Stanley was developed for high‐speed desert driving without manual intervention. The robot's software system relied predominately on state‐of‐the‐art artificial intelligence technologies, such as machine learning and probabilistic reasoning. This paper describes the major components of this architecture, and discusses the results of the Grand Challenge race. © 2006 Wiley Periodicals, Inc.

This paper evaluates the suitability of the MapReduce model for multi-core and multi-processor systems. MapReduce was created by Google for application development on data-centers with thousands of servers. It allows programmers to write functional-style code that is automatically parallelized and scheduled in a distributed system.We describe Phoenix, an implementation of MapReduce for shared-memory systems that includes a programming API and an efficient runtime system. The Phoenix runtime automatically manages thread creation, dynamic task scheduling, data partitioning, and fault tolerance across processor nodes. We study Phoenix with multi-core and symmetric multiprocessor systems and evaluate its performance potential and error recovery features. We also compare MapReduce code to code written in lower-level APIs such as P-threads. Overall, we establish that, given a careful implementation, MapReduce is a promising model for scalable performance on shared-memory systems with simple parallel code.

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