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
3D reconstruction
PLVS code released
I am happy to release the code of my project PLVS. PLVS is a real-time system that leverages sparse RGB-D and Stereo SLAM, volumetric mapping, and
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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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PLVS for Circus Maximus Mixed-Reality Experience
If you are visiting Rome, now you can go to the Circus Maximus and enjoy a great Mixed-Reality experience. An
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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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PLVS: An Open-Source RGB-D and Stereo SLAM for volumetric reconstruction and 3D incremental segmentation
I am working hard during my free time and having fun with PLVS to finalize the outcoming paper: PLVS: An
Read morePLVS: An open-source SLAM system with Points, Lines, Volumetric mapping and 3D incremental Segmentation
PLVS(*) is a real-time system that leverages sparse RGB-D and Stereo SLAM, volumetric mapping, and 3D unsupervised incremental segmentation. PLVS
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@ Inglobe Technologies
From May 2018 I am Chief Research Officer at Inglobe Technologies. I will lead R&D activities on Computer Vision, Machine
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TRADR Techday 2017
On November 17 2017, I was a member of the crew who organized the TRADR Technology Day. More than 60
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Fourth TRADR Evaluation Exercise in Rotterdam with GB Firefighters
From November 6th to November 17th, I took part to the fourth TRADR Evaluation Exercise in Rotterdam. The full TRADR system was evaluated by the GB firefighters end-users
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TRADR on EuroNews
The TRADR EU project is on Euronews. The video report shows the simulated disaster scene we used in the last
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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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RGBDTAM: A Cost-Effective and Accurate RGB-D Tracking and Mapping System
In this post, Alejo Concha Belenguer presents his new work RGBDTAM. This is a direct SLAM pipeline that runs in real-time and
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