The Ammazing iOS6 Maps

omg: somebody painted the landscape in white.
Three types of image quality in southern switzerland.

The Helsinki Central Railway Station has magically turned into a park


Helsinki’s biggest open-air marketplace (Kauppatori) has also been magically been turned into a park (along with a bunch of other marketplaces)

Walking across there seems like fun…

Apple thinks that much of the east side of Portland, Oregon is a nature park

New Orleans famously misplaced Crescent City Connection bridge, otherwise known as the Greater New Orleans Bridge to no one

New Orleans famous Huey Long Bridge now available at Home Depot

Apple seems to have left out half of one of Southeastern Europe’s biggest tourist destinations

Apparently Belgrade, Serbia has no rivers

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Visualizing large scale Uber Movement Data

Last month one of my acquaintances in LinkedIn pointed me to a very interesting dataset. Uber's Movement Dataset. It was fascinating to explore their awesome GUI and to play with the data. However, their UI for exploring the dataset leaves much more to be desired, especially the fact that we always have to specify source and destination to get relevant data and can't play with the whole dataset. Another limitation also was, the dataset doesn't include any time component. Which immediately threw out a lot of things I wanted to explore. When I started looking out if there is another publicly available dataset, I found one at Kaggle. And then quite a few more at Kaggle. But none of them seemed official, and then I found one released by NYC - TLC which looked pretty official and I was hooked.
To explore the data I wanted to try out OmniSci. I recently saw a video of a talk at jupytercon by Randy Zwitch where he goes through a demo of exploring an NYC Cab dataset using OmniSci. A…

ARCore and Arkit, What is under the hood: SLAM (Part 2)

In our last blog post (part 1), we took a look at how algorithms detect keypoints in camera images. These form the basis of our world tracking and environment recognition. But for Mixed Reality, that alone is not enough. We have to be able to calculate the 3d position in the real world. It is often calculated by the spatial distance between itself and multiple keypoints. This is often called Simultaneous Localization and Mapping (SLAM). And this is what is responsible for all the world tracking we see in ARCore/ARKit.
What we will cover today:How ARCore and ARKit does it's SLAM/Visual Inertia OdometryCan we D.I.Y our own SLAM with reasonable accuracy to understand the process better Sensing the world: as a computerWhen we start any augmented reality application in mobile or elsewhere, the first thing it tries to do is to detect a plane. When you first start any MR app in ARKit, ARCore, the system doesn't know anything about the surroundings. It starts processing data from cam…

ARCore and Arkit: What is under the hood : Anchors and World Mapping (Part 1)

Reading Time: 7 MIn
Some of you know I have been recently experimenting a bit more with WebXR than a WebVR and when we talk about mobile Mixed Reality, ARkit and ARCore is something which plays a pivotal role to map and understand the environment inside our applications.
I am planning to write a series of blog posts on how you can start developing WebXR applications now and play with them starting with the basics and then going on to using different features of it. But before that, I planned to pen down this series of how actually the "world mapping" works in arcore and arkit. So that we have a better understanding of the Mixed Reality capabilities of the devices we will be working with.
Mapping: feature detection and anchors Creating apps that work seamlessly with arcore/kit requires a little bit of knowledge about the algorithms that work in the back and that involves knowing about Anchors. What are anchors: Anchors are your virtual markers in the real world. As a develope…

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