vortex-auv
xivo
vortex-auv | xivo | |
---|---|---|
2 | 2 | |
80 | 828 | |
- | 0.0% | |
8.6 | 0.0 | |
16 days ago | about 1 year ago | |
Python | C++ | |
MIT License | GNU General Public License v3.0 or later |
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vortex-auv
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Did recent AI events change your life plans?
https://github.com/vortexntnu/vortex-auv (both these are tethered but if you want untethered you can find them in the commercial defence area easily)
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Open-source Autonomy Software in Rust-lang with gRPC for the Roomba series robot vacuum cleaners
u/Khay_ That would be awesome, you are very much welcome to contribute :) I have a lot of thoughts and ideas for the project, but haven't mapped it out yet. What if I tried to set up a backlog for the project as chronological steps and some overall goals, and you can have a look? I have done something similar in the past, but only for an autonomous underwater vehicle written in C++ and Python using ROS (have a look here), and learned a lot from that. I would like to create something similar, but I know a lot of things that can improve from the past architecture.
xivo
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Unsupervised Depth Completion from Visual Inertial Odometry
Hey there, interested in camera and range sensor fusion for point cloud (depth) completion?
Here is an extended version of our [talk](https://www.youtube.com/watch?v=oBCKO4TH5y0) at ICRA 2020 where we do a step by step walkthrough of our paper Unsupervised Depth Completion from Visual Inertial Odometry (joint work with Fei Xiaohan, Stephanie Tsuei, and Stefano Soatto).
In this talk, we present an unsupervised method (no need for human supervision/annotations) for learning to recover dense point clouds from images, captured by cameras, and sparse point clouds, produced by lidar or tracked by visual inertial odometry (VIO) systems. To illustrate what I mean, here is an [example](https://github.com/alexklwong/unsupervised-depth-completion-visual-inertial-odometry/blob/master/figures/void_teaser.gif?raw=true) of the point clouds produced by our method.
Our method is light-weight (so you can run it on your computer!) and is built on top of [XIVO] (https://github.com/ucla-vision/xivo) our VIO system.
For those interested here are links to the [paper](https://arxiv.org/pdf/1905.08616.pdf), [code](https://github.com/alexklwong/unsupervised-depth-completion-visual-inertial-odometry) and the [dataset](https://github.com/alexklwong/void-dataset) we collected.
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[N][R] ICRA 2020 extended talk for Unsupervised Depth Completion from Visual Inertial Odometry
Our method is light-weight (so you can run it on your computer!) and is built on top of XIVO our VIO system.
What are some alternatives?
ORB_SLAM3 - ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM
open_vins - An open source platform for visual-inertial navigation research.
rmw_ecal - Please visit the new repository: https://github.com/eclipse-ecal/rmw_ecal
rtabmap - RTAB-Map library and standalone application
Dstar-lite-pathplanner - Implementation of the D* lite algorithm in Python for "Improved Fast Replanning for Robot Navigation in Unknown Terrain"
unsupervised-depth-completion-visual-inertial-odometry - Tensorflow and PyTorch implementation of Unsupervised Depth Completion from Visual Inertial Odometry (in RA-L January 2020 & ICRA 2020)
turtlebot3_simulations - Simulations for TurtleBot3
void-dataset - Visual Odometry with Inertial and Depth (VOID) dataset
OpenBangla-Keyboard - An OpenSource, Unicode compliant Bengali Input Method
r3live - A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package
evdevhook - libevdev based DSU/cemuhook joystick server
Open3D - Open3D: A Modern Library for 3D Data Processing