ORB_SLAM3
open_vins
ORB_SLAM3 | open_vins | |
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15 | 5 | |
6,031 | 1,992 | |
2.4% | 2.1% | |
0.0 | 6.9 | |
20 days ago | 4 months ago | |
C++ | C++ | |
GNU General Public License v3.0 only | GNU General Public License v3.0 only |
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ORB_SLAM3
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How to make bot that can navigate to instructed positions in a 3d world using only rgb images
Thank you for the recommendation. Other's I've talked to have also mentioned slam. I think I might use this repo that looks promising: https://github.com/UZ-SLAMLab/ORB_SLAM3
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Bot that can navigate to instructed positions in a 3d world
https://github.com/UZ-SLAMLab/ORB_SLAM3 looks promising. Thank you for the recommendation!
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Any links/tutorials on how to integrate a mono cam with ros noetic for slam?
ORB_SLAM3 on GitHub: https://github.com/UZ-SLAMLab/ORB_SLAM3
- How to implement SLAM from scratch in C++
- ORB-SLAM3 memory leak
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Wiring in progress
Yeah pretty much. I'd say it can still be useful if your custom application only needs one or two new modules and can otherwise be formed from existing code. It's great if you make a lot of different systems with similar sub-modules. The bigger research institutions also open source a lot of their code with ROS integrations, so you can just drop in state of the art modules where appropriate. e.g. OrbSlam3,
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Doing SLAM with laptop sensors
You could try your luck with OrbSLAM3 or Kimera, they're pretty close to state of the art and open source. Might have a bit of a learning curve though.
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What's the current SOTA for vSLAM?
I hear good things about stereo-visual + inertial and OrbSlam3 seems pretty hot, but this is a couple years old now and I'm not active enough in the field to give a definitive opinion.
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Advice regarding software for indoor autonomous MAV/drone
I'm currently in the planning stage of building an indoor autonomous MAV/drone. Up until this point I thought this would require a SLAM solution, such as ORB-SLAM3, but now I see this framework by Intel which doesn't mention SLAM anywhere. All of the literature seems to be about SLAM so maybe I just don't know the terminology for what I'm looking for.
- Software advice for inside-out cave mapping
open_vins
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Modular Open Source Visual SLAM
From what I have understood after reading research papers related to the VSLAM, the modularity aspect is not easy to achieve given the extracted features and descriptors are intrinsically linked with feature matching and handling of map points. I would like to know if there are some good Open Source VSLAM projects available which can be used with different feature extractors so I can get a comparative results with respect to just changing the feature extractors . I have tried pyslam project which is actually quite good considering the modularity but as the author himself points out this is only for academic purposes and when I compared the results of ORB_SLAM2 feature extractor using this module vs the original ORB_SLAM2 for KITTI data set , the results are not comparable. I am also looking into OpenVINS ( and from initial reading it is also using ORB Features, although it does have a base Tracker class which can be modified to create a new Tracker with different descriptor) If anyone has worked with custom feature extractor incorporated into prebuilt SLAM pipeline and can guide me as to how to proceed with the implementation of custom Feature extractor into a SLAM Front end using a Open Source VSLAM framework, it will be really helpful.
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SLAM vs. Visual Odometry Approaches
Because the standard MSCKF is the only one that doesn't contain the map points in the state. Note that this is only for the standard MSCKF. More modern MSCKFS variations like OpenVINS will actually add some SLAM features because it improves the accuracy.
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Advances in SLAM since 2016
Aside from that there have been some publications of some high quality open source SLAM systems like OpenVINS and ORB-SLAM3.
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Sfm or slam pseudo code
Check out open vins. Its an implementation of the vins slam project. https://github.com/rpng/open_vins
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Visual Odometry or SLAM with pose uncertainty output
Generally you want to use a Kalman Filter based method if you want access to the uncertainties. This is because it is much easier to extract a subset of the covariance in the kalman filter form. I would recommend OpenVins. One of the best open source visual odometry projects, and it is pretty well documented.
What are some alternatives?
openvslam - OpenVSLAM: A Versatile Visual SLAM Framework
rtabmap - RTAB-Map library and standalone application
SuperGluePretrainedNetwork - SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)
Udacity-self-driving-car-engineer-P6-Kidnapped-Vehicle - 优达学城无人驾驶工程师纳米学位P6--被绑架的汽车--定位
msckf_vio - Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight
VINS-Mono - A Robust and Versatile Monocular Visual-Inertial State Estimator
xivo - X Inertial-aided Visual Odometry
vortex-auv - Software for guidance, navigation and control for the Vortex AUVs. Purpose built for competing in AUV/ROV competitions.
SuperPoint_SLAM - SuperPoint + ORB_SLAM2
Kalman-and-Bayesian-Filters-in-Python - Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.