I am programming my iRobot Create robot with ROS, but I have not found the wheel encoders in ROS.
Is it possible to use encoders for the iRobot Create robot in ROS?
In general yes. See the answer to your question on answers.ros.org for more details.
Related
I have trained the classification model on Nvidia GPU and saved the model weights(checkpoint.pth). If I want to deploy this model in jetson nano and test it.
Should I convert it to TenorRT? How to convert it to TensorRT?
I am new to this. It would be helpful if someone can even correct me.
The best way to achieve the way is to export the Onnx model from Pytorch.
Next, use the TensorRT tool, trtexec, which is provided by the official Tensorrt package, to convert the TensorRT model from onnx model.
You can refer to this page: https://github.com/NVIDIA/TensorRT/blob/master/samples/opensource/trtexec/README.md
The TRTEXEC is a more native tool that you can take it from NVIDIA NGC images or downloading from the official website directly.
If you use a tool such as torch2trt, it is easy to encounter the operator issue and complicated to resolve it indeed (if you are not familiar to deal with plugin issues).
You can use this tool:
https://github.com/NVIDIA-AI-IOT/torch2trt
Here are more details how to implent a converter to a engine file:
https://github.com/NVIDIA-AI-IOT/torch2trt/issues/254
The Watson Machine Learning service provides three options for training deep learning models. The docs list the following:
There are several ways to train models Use one of the following
methods to train your model:
Experiment Builder
Command line interface (CLI)
Python client
I believe these approaches will differ with their (1) maturity and (2) the features they support.
What are the differences in these approaches? To ensure this question meets the quality requirements, can you please provide a objective list of the differences? Providing your answer as a community wiki answer will also allow the answer to be updated over time when the list changes.
If you feel this question is not a good fit for stack overflow, please provide a comment listing why and I will do my best to improve it.
The reasons to use these techniques depends on a user's skillset and how they are fitting the training/monitoring/deploying steps into their workflow:
Command Line Interface (CLI)
The CLI is useful for quick and random access to details about your training runs. It's also useful if you're building a data science workflow using shell scripts.
Python Library
WML's python library allows users to integrate their model training+deployment into a programmatic workflow. It can be used both within notebooks as well as via IDEs. The library has become the most widely used way for executing batch training experiments.
Experiment Builder UI
This is the "easy button" for executing batch training experiments within Watson Studio. It's a quick way to learn the basics of the batch training capabilities in Watson Studio. At present, it's not expected that data scientists would use Experiment Builder as their primary way of starting batch training experiments. Perhaps as Model Builder matures, this could change but the Python library is more flexible for integrating into production workflows.
I want to implement Structur from Motion (SfM) / Simultaneous Localization and mapping algorithms using my webcam. I am very new on this topic so I need advices from experts in the internet. I could now able to build OpenCV opencv sfm tutorial for this purpose and I looked OpenSFM but it seems like just a GUI. What other open libraries/programs that I can use for this task? any suggestions/advices/tutorials are appreciated.
I am struggling to create a custom haar classifier. I have found a couple tutorials on the web, but they do not specify which version of opencv they are using. What I need is a very concise and simplified example of the steps that are required, along with a simple dataset of images. I also need to know the opencv version and the OS platform so I can get it running. I have tried a matrix of opencv versions on both windows and linux and I have run into memory error after memory error. I would like to start with a known good set of data and simple commands before expanding it to fit my problem.
Thanks for your help,
Chris
OpenCV provides two utility commands createsamples.exe and haartraining.exe, which can generate xml files used by Haar Classifiers. That is, with the xml file outputted from haartraining.exe, you can directly use the face detection sample with your xml file to detect any customized objects.
About the detailed procedures to use the commands, you may consult Page 513-516 in the book "Learning OpenCV", or this tutorial.
About the internal mechanism of how the classifier works, you may consult the paper "Rapid Object Detection using a Boosted Cascade of Simple
Features", which has been cited 5500+ times.
I need to process DICOM formatted medical images and visualize them in 3D, also do some image processing on these images on real-time. Therefore, I am asking this question to learn which SDK has better real-time characteristics for medical visualization and image processing?
The Visualization Toolkit (VTK) is an open-source, freely available software system for 3D computer graphics, image processing and visualization.
You can find details here.
Or another solution would be the modifying or utilizing 3D engine that supports volume rendering.
Moreover, for computer vision algorithms, OpenCV seems promising.
osgVolume is an add-in to the popular openscenegraph library for doing this
Just use GDCM+VTK. In 2D simply use gdcmviewer. In 3D you need to build gdcmorthoplanes.
Ref:
http://sourceforge.net/apps/mediawiki/gdcm/index.php?title=Gdcmviewer
http://sourceforge.net/apps/mediawiki/gdcm/index.php?title=Using_GDCM_API
You could check out MITK (http://mitk.org) which combines the already mentioned VTK with the Insight Toolkit (http://www.itk.org) for image processing. Another option to start from could be Slicer (http://www.slicer.org), but this depends on the license you need.
In a uni we were taught Matlab for DICOM file processing. I think it has pretty nice and easy to use plugins for that as well. The end results were that using Matlab I was able to do all kinds of DICOM image processing, filtering and so forth.
As you probably know, Matlab is not SDK but a complete environment. Nevertheless you can write scripts to achieve normal application behavior: Create windows, buttons, images, etc.