I want to use dataset at https://ingmec.ual.es/datasets/lidar3d-pf-benchmark/ in my project. The available map is .simplemap. What I understand is it stores both map and the robot poses as well. I want to get the point cloud representation of this map (which later I can convert into octomap) as well as vehicles ground truth pose in the map.
I have been able to get the CPose3DPDF from which I obtained CPose3d which I believe is the desired vehicle's ground truth pose. Please correct me if I am wrong. Now I have two problems. First the length of trajectory is just 97 which makes me suspicious about my code to obtain it. Second is about the CSensoryFrame which I obtain along with CPose3DPDF. When I get CObservation by doing CSensorFrame->getObservationByIndex and write to a file, it gives me idea that it stores velodyne readings. But I am unable to recover point cloud from it. Could anyone please guide me to a tool which can convert a .simplemap into a point cloud or an octomap representation and obtain vehicle's pose out of it as well. Many thanks in advance.
For the records: this one was answered here:
Your assumptions were all correct.
I realized the full UAL campus map was not included into the downloads. It's now available to download inside 2018-02-26-ual-campus-map.zip, at the bottom of this dataset page.
You can also regenerate the pointcloud, octomap from the .simplemap using the app application-observations2map.
Example .ini files can be found under MRPT/share/mrpt/config_files/*
You can also visually inspect .simplemap files with the robot-map-gui app.
Related
I'm trying to build a map for to show spending. Our vendor data is not entirely clean and we could have 5 or 6 definitions of the same place. We also have hierarcial POI issues also (think gives an entire shopping strip geometry instead of individual store). I'd like to know if there are any common algorithms to identity duplicates at the geometry object level.
My data are in Geopandas Dataframes with a 2-D polygon to represent a place, and other meta data (i.e. store name, category, etc.). Sometimes it will come with an address but not always and not a very consistent postal format.
I'm working in Python specifically, but open to any suggestions, not exactly limited to Python. Thank you in advance for any help or suggestions.
I am currently exploring the notion of using iris in a project to read forecast grib2 files using python.
My aim is to load/convert a grib message into an iris cube based on a grib message key having a specific value.
I have experimented with iris-grib, which uses gribapi. Using iris-grib I have not been to find the key in the grib2 file, althrough the key is visible with 'grib_ls -w...' via the cli.
gribapi does the job, but I am not sure how to interface it with iris (which is what, I assume, iris-grib is for).
I was wondering if anyone knew of a way to get a message into an iris cube based on a grib message key having a specific value. Thank you
You can get at anything that the gribapi understands through the low-level grib interface in iris-grib, which is the iris_grib.GribMessage class.
Typically you would use for msg in GribMessage.messages_from_filename(xxx): and then access it like e.g. msg.sections[4]['productDefinitionTemplateNumber']; msg.sections[4]['parameterNumber'] and so on.
You can use this to identify required messages, and then convert to cubes with iris_grib.load_pairs_from_fields().
However, Iris-grib only knows how to translate specific encodings into cubes : it is quite strict about exactly what it recognises, and will fail on anything else. So if your data uses any unrecognised templates or data encodings it will definitely fail to load.
I'm just anticipating that you may have something unusual here, so that might be an issue?
You can possibly check your expected message contents against the translation code at iris_grib:_load_convert.py, starting at the convert() routine.
To get an Iris cube out of something not yet supported, you would either :
(a) extend the translation rules (i.e. a Github PR), or
(b) sometimes you can modify the message so that it looks like something
that can be recognised.
Failing that, you can
(c) simply build an Iris cube yourself from the data found in your GribMessage : That can be a little simpler than using 'gribapi' directly (possibly not, depending on detail).
If you have a problem like that, you should definitely raise it as an issue on the github project (iris-grib issues) + we will try to help.
P.S. as you have registered a Python3 interest, you may want to be aware that the newer "ecCodes" replacement for gribapi should shortly be available, making Python3 support for grib data possible at last.
However, the Python3 version is still in beta and we are presently experiencing some problems with it, now raised with ECMWF, so it is still almost-but-not-quite achievable.
So, a little bit on my problem.
TL;DR
Can I use machine-learning instead of Elastic Search to find results depending on the user's text input? Is it a good idea?
I am working on a car spare parts project, and we have split the car into 300 parts that we store on the database, with some data for each part (weight, availability, etc).
When the customer inputs the text of his part, we need to be able to classify the part, and map it to one in our database.
The current way it's being done is by people on our team manually mapping the customer text with the parts on our database, we want to automate that process.
We tried using MongoDB text search, but it was often inaccurate since parts have different names in different parts of the country.
So we wanted something that got more accurate results, and improved by the more data we have, we immediately considered TensorFlow, after some research and taking part of Google's Machine Learning Crash Course, I got to that point where it specified:
Models can't learn from string values, so you'll have to perform some feature engineering to convert those values to something numeric
That would be useful in the case we have limited number of features as strings, but we don't know what the user will input as a text.
So, my questions are:
1- Can we use Machine Learning to map text input by the user with some documents on our database?
2- If we can do that, is it a good idea to favor it over other search tools like ElasticSearch?
3- Can ElasticSearch improve its results the more data we have? How?
4- How would you go about this problem?
Note: I'd be doing that in Node.js, and since TensorFlow.js is new, I am inclining to go for other solutions, but if push comes to shove, and the results are much better, I would definitely go there.
TL;DR: Yes and yes.
TS;WM:
This is a perfectly suited problem for machine learning. Especially so, if you have a database of past customer texts that have already been mapped to parts. Ideally, you have hundreds of texts mapped to each part. If that is present, you can design and train a network. And models can learn from string values with some engineering, and it's not that bad.
I'm not sure ElasticSearch would improve much on the network. I don't know much about auto parts trading, but as a wild guess, "the large round thingy that helps change direction" would never be mapped to "steering wheel" by ES but could be learned easily by a network - provided there are at least some examples of people using that text to specify steering wheel.
You can but don't have to necessarily use tensorflow.js for your network. The AI could run on your server as a webservice, and you'd just send over the customer's text to it and it would send back it's recommendations of part SKUs and names.
Wondering, how do I recognize if an image contains a specific object and this object is completely visible (not partially).
Cognitive Services Computer Vision API provides set of tags and description of the image I send, however, there is no information if object is completely or partially represented.
My goal is to have a service that I can upload a picture of, say, car, and get information is it a full car visible or just part of it.
Unfortunately the Computer Vision API is currently unable to perform such a function.
The tags returned do have a 'score' which represents the confidence that this item is in the image. You may find there's some correlation between the confidence and how much of the item is in the image, but you'd need to run some experiments to see how well it matches up. If the object is obscured too much, it may not even detect it all.
Feel free to drop a suggestion on our User Voice, if you think this would be a useful feature.
I am using spatstat package in R to read my road network shapefile which also has some additional attributes.
When i am reading my shapefiles and converting them to as.psp(before I make them an object of linnet), I am getting n columns of data frame discarded. I do not understand why? The columns being discarded are my covariates for a linear network, so I am not able to bring them into my analysis.
Could someone give me an idea why this happens and how to correct it?
Why it happens:
I would guess that we (spatstat authors) need to spend a bit of time discussing with the maptools guys how to handle the additional info in the SpatialLinesDataFrame object, and we haven't done that yet.
How to correct it:
You have to write some code on your own at the moment. You can extract the data from SpatialLinesDataFrame object by accessing the #data slot. Please provide specific data and how you need to use the additional data (what format do you need it in) if you need more help. You can find a few helpful commands here: https://cran.r-project.org/web/packages/spatstat/vignettes/shapefiles.pdf