Deep_Object_Pose VS OutRun

I've been doing some research in this area and there are a few deep learning solutions to this problem. For example, NVIDIA's Deep Object Pose Estimation will estimate the 6DOF pose of a known object. But you'll have to train the network if you want to detect a new object. PoseCNN, which someone else mentioned, does a similar thing. CenterPose is more interesting, as it can estimate then pose of an object from a known category; e.g. sneakers, or laptops, rather that one specific object (as DOPE and PoseCNN do).

For our last event of ArchE Week, the Ohio State Underwater Robotics Team (Website, Instagram) is hosting a workshop tonight on machine learning! The workshop is an interactive walkthrough of using machine learning solutions to make predictions. Some example problems we could be trying to solve are predicting a grade, predicting the weather, and the classic recognize a digit problem. Our team personally uses machine learning to do real-time object detection with YOLO and NVidia DOPE, so we may touch on that as well!

Ive had a similar problem to you, but I have mostly solved it by using the Outrun app (which is free and open source), and going into the settings and turning up the GPS smoothing. I mostly run in central london, and it seems to work with the tall buildings here.

https://github.com/timfraedrich/OutRun

https://github.com/timfraedrich/OutRun/issues/91

> I wouldn't necessarily say abandoned. I still work on it from time to time, but progress is very very slow and I cannot prioritise it over other things atm

Shout out to the app "Out-Run" that I use to track my rides. Privacy focused way to track your rides without all of the bullshit social media that comes with Strava. No account, all data stored locally, can easily export your rides, no data collected. Have exchanged a few emails with the dev and he's a super cool dude, so I shout out his app at every opportunity. All open source as well.

This is the GitHub page.