![]() today announced the recipients of approximately $1 million in academic research funding. I am very happy to announce that The ] project is the recipient of an academic research gift from Microsoft, see the ]. BTW, you can also see these and other photos on a ]. I took bracketed exposures for each few, so one day I might find the time to create ] out of them. \n\n\n\n\n\nOne day in November Metz was enveloped in a dense fog, so I went out and took about a 100 photos, check out the best at my Flickr page by clicking on the thumbnails above. Hence, use at your own risk ! If you find bugs and you have a fix, I’ll gladly incorporate the fix in the code.\n\nTo get a feel for what is here, take a look at kmeansdemo and at //~EMintro.m//, which produced the figures in my ] \n\n * ]\n * ]\n * ]\n * ]\n\n\n\n\n However, while I think the code is correct, the code has not been exercised a whole lot since my group and I switched to working in ]. As of version 2, it contains ] of Gaussian mixtures with automatic selection of the number of components. It is not very extensive ! For now, only k-means clustering is implemented and a slow agglomerative procedure. Matlab Clustering Package, Version 2\n\nA collection of matlab routines to do clustering. If you just want to use it, not implement it, here is MATLAB code.\n The method is applicable when at least seven landmarks are seen from three different vantage points, whether by one robot that moves over time or by multiple robots that observe a set of common landmarks.\n\nHere is a link to the ICRA paper, but if you want to implement this you might want to look at the technical report instead. The algorithm substantially enlarges the scope in which non-linear batch-type SLAM algorithms can be applied. This linear estimate can then, if desired, be fine-tuned using 2D bundle adjustment. Ashley Stroupe did a series of experiments with the Winnow Robots in order to validate the method experimentally, which is also described in the paper.\nThe method supplies a good initial estimate of the geometry, even without odometry or in multiple robot scenarios. Below are my colleagues in IS (click on the mugshots to claim your prize):\n\n]]]]]]]\n]]]]]]]įrank Dellaert and ]\n\nAt the 2002 ICRA meeting, I presented a paper on how computer vision techniques can be applied to the bearings-only ] (]) problem, in order to obtain a linear algorithm that recovers both robot poses and observed landmarks. Gray lines indicate recorded lines of sight, which complement the trajectory information:\n\n]]\n]]\n]]\nīoth the ] and the ] are part of the ] at the CoC. Shown are the traced trajectories at regular time intervals between steps, which collectively constitute a map of the empty space, and hence of the navigable environment. The pictures below show a simulated example where 15 robots are released on the left and execute a pure random walk control strategy in a large environment, except that they re- flect off walls. \nIntrinsic Localization and Mapping or Diffusion Mapping is an approach where a highly redundant team of simple robots is used to map out a previously unknown environment, simply by virtue of recording the localization and line-of-sight traces, which provide a detailed picture of the navigable space.
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