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
deep learning
pyslam v2.1 is available
I am happy to share the new version of pyslam: v2.1. It comes with new improvements and the following new
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slamplay is ready!
If you love to play with SLAM like me, I shared slamplay on github: https://github.com/luigifreda/slamplay. slamplay is a collection of powerful
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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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Visual Perception and Spatial Computing
On February 2019, I was the teacher of the course Visual Perception and Spatial Computing in the Advanced School of AI. This school
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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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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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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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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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History of Neural Network Design
From Eugenio Curlurciello’s blog, Professor at Purdue University, you can find here a post about the the history of neural network design
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