A new release of pySLAM is here. Loop-closing, volumetric integration, depth prediction and more improvements.
🎉 pySLAM v2.2.5 is here! This new release includes a visual SLAM pipeline for monocular, stereo, and RGBD cameras with:
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Luigi Freda
Robotics & Computer Vision Engineer, PhD
Machine Learning
My new pySLAM v2 is out!
I am excited to release pySLAM v2. The new version allows you to play with SLAM techniques, visual-odometry, keyframes, bundle-adjustment,
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Hottest Deep Learning Frameworks 2018
In this very nice article, Jeff compares all the hottest deep learning frameworks that are used in the Academic and
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CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction
In this page, the Authors K. Tateno, F. Tombari, I. Laina and N. Navab present the following paper in which CNNs are used
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Hand-Crafted Features or Learned Local Features?
In this post, Torsten Sattler presents his upcoming CVPR 2017 paper “Comparative Evaluation of Hand-Crafted and Learned Local Features”
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Optimization – why momentum really works
This post explains why momentum works in optimization. Nice interactive images are used to this aim. Here you can find another
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Driverless cars that ‘see’ the road ahead
Micheal Milford explains how to make a driverless car see the road ahead. From his Google+ post: “We were asked to
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Do we need self-driving cars?
This post reports that a self-driving Uber car was involved in a high-speed crash in Tempe, Arizona. No one was
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Bias and variance tradeoff
This very nice post clearly explains the bias and variance tradeoff http://www.learnopencv.com/bias-variance-tradeoff-in-machine-learning/ This tradeoff can be mathematically stated by the
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ElasticFusion – Real-time Light Source Detection
A new journal paper about ElasticFusion has come out from Davison’s group. New exciting results on 3D light source mapping are presented.
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Probabilistic Deep Network
This post explains a trick which allows us to convert neural network outputs into probabilities, with no cost to performance,
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Are the Sceptics Right? Limits and Potentials of Deep Learning in Robotics
This post presents an interesting summary of the RSS 2016 Workshop Here you can fine official page and description of the
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Tiny-CNN: Header-only, Dependency-free Deep Learning Framework
Tiny-CNN is an header-only, dependency-free deep learning framework for C++11. Just include tiny_cnn.h and write your model in C++. There is
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Deep Learning for Robust Normal Estimation in Unstructured Point Clouds
Normal estimation in point clouds is a crucial first step for numerous algorithms, from surface reconstruction and scene understanding to
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Long Short-Term Memory Networks (LSTMs)
In this nice post, LSTMs are described to have more control than typical RNNs over what they remember. In general,
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