I built an apk using the HelloAR scene (which is provided with ARcore package). The app is only detecting Horizontal surface like table and creates it's own semi-transparent plane over it. When I moved my phone around a bottle, the app again, only created a horizontal plane cutting through the bottle. I expected ARCore to create planes along the bottle as I move my phone around, like polygons in a mesh.
Another scenario is, I placed 2 books on the floor, and each of them have different thickness. But the HelloAR app creates only one semi-transparent horizontal surface over the thicker book, instead of creating two surfaces (one for each book).
What is going wrong here? How can I fix it and make the HelloAR app work more precisely? Please help.
Software: Unity v2018.2,
ARcore v1.11.0
ARCore generates an approximate point cloud using a soft movement of the device to identify the featured points, this points are detected by contrast in the different shapes, if you use your application in test mode in unity you can see how the points are placed in your empty scene.
Once the program has enough points at the "same height" (I don't know the exact precision), it generates the plane that you can see, but it won't detect planes separated by a difference of 5cm or even more distance.
If you want to know the approximate accuracy of the app, test it with unity and make a script to capture the generated points that have been used to generate the planes, then check the Y difference to see which is the tolerance distance.
Okay so Vuforia is currently one of the leading SDKs for augmented reality providing a wide area of detection options (Images, Ground, Point, 3D objects, ...)
So regarding your question about detecting a bottle I would most certainly use the 3D model detection feature. You can read the official docs here.
You need to first generate an approximate of the object in a 3d modeling software and the use their program to generate the detection model. Then you put this in Unity and setup the detection. (no coding needed)
I have some experience with this kind of detection. I used it to detect a large 2mx2m scale model of an electric vehicle. It works great, you can walk around it and it tracks it through and through. You can see a short official demo here
Hope it helped to explain this in short!
Related
Via ARKit, I want to place indoor map on floor.
Currently I tried 2 things:
I've placed large Plane below camera and above floor, But it causes quite drift. Does not move well when we walk, and overall experience is not overwhelming.
Saw a solution where you can identify horizontal plane, but it has its own issues.
So is it really possible with good results?
Devices with LiDAR
The LiDAR scanner has its advantages and disadvantages. The main advantage of LiDAR is its ability to almost instantly reconstruct floor and walls, then you can easily attach any 3D model to the resulted surface – a model will be stable, it will not drift, so a user's AR experience will be overwhelming, as you said. Also, an important advantage of LiDAR is the excellent performance in environment with poor lighting and with poor textures.
Here you can read about Occlusion feature and some of the LiDAR peculiarities. Good news: LiDAR perfectly works in conjunction with the Plane Detection option.
ARKit subdivides the reconstructed scene into ARMeshAnchors which give you access to polygonal geometry and surface classification.
ARMeshAnchor().geometry.classification
ARMeshAnchor().geometry.faces
ARMeshAnchor().geometry.vertices
ARMeshAnchor().transform.columns.3
Devices without LiDAR
In the absence of a LiDAR scanner, we can only detect horizontal and vertical surfaces using the Plane Detection feature. I can say that all AR frameworks (including ARKit and RealityKit) are much better and faster in defining horizontal surfaces, as opposed to vertical ones.
However, Detected Planes are less stable compared to Reconstructed Surfaces, and therefore, a slight drifting is possible sometimes. To successfully complete the Plane Detection stage, you need a well-lit room and good-for-tracking surrounding objects' textures.
ARKit calls your delegate's renderer(_:didAdd:for:) with a ARPlaneAnchor for each unique vertical and/or horizontal surface. And each plane anchor provides details about the surface – its world position, dimensions and real-world surfaces' classification.
In addition to the above, the delegate method called renderer(_:didUpdate:for:) is required to merge multiple coplanar Detected Planes into bigger resulting Detected Plane (a surface of a floor, for example).
ARPlaneAnchor().classification
ARPlaneAnchor().extent
ARPlaneAnchor().alignment
ARPlaneAnchor().center
Is it really possible with good results?
Yes, in both cases, it's possible to attach a map without drifting – whether you're using Plane Detection or Scene Reconstruction.
I'm making an augmented reality app with Unity and Mapbox for both ios and android. I have data sets that I am using to make markers in the real world when someone uses the app. I collected json files and converted them to geojson files and then I made a custom map in Mapbox Studio with these 4 different geojson files. Basically I want to have the markers from the datasets I collected to show up in the real world. I am not sure how to get these markers to show up in the real world and not with building prefabs. Example of my custom app made in Mapbox. Each color shows a different category of markers. There are four categories.
Here is an example of what I am referring to.
In this image skeletons can show up in the real world.
Here is an example of what I am not referring to.
In this image droids are place in a map but it is not the real world. It is like Pokemon Go where the map is generated with location but you don't actually see the real world when you are playing.
I already have my Unity project set up and this is the final step, but I am just having issues getting it to show up in the real world. So far, tutorials only show on to get it to reflect something like Pokemon Go.
You will have one scene with a stationary Camera. Your code will monitor the MapBox data in Update(), constantly passing the current GPS position and receiving your list of markers/points of interest. You can simply randomly spawn skeletons in a sphere area (see https://docs.unity3d.com/ScriptReference/Random-insideUnitSphere.html) around the Camera's transform position once you detect that the user's GPS position is in within a certain distance of the center of your point of interest. Keep track of that list, and destroy the skeletons once they leave the area - and have some way of making sure you only spawn them once for that area.
Your skeletons should have a NavMeshAgent, and you should generate a NavMesh onto the ARFoundation plane for them to walk on. In this case, the plane is probably dynamically created and you may need to use the dynamic NavMesh component https://github.com/Unity-Technologies/NavMeshComponents. If you tell the NavMeshAgent to go to a specific point it will walk to the closest point - so even though you get a random position in the sphere in 3D, the skeleton will move or spawn onto the nearest point so there is no need to figure out how to convert it to the 2D plane space.
Your AR view, both the tracking of the camera position/angle and the generation of a plane representing the ground, will be something generated by ARFoundation and it is simple to add the basic functionality. They have a prefab that already includes the camera and generates the plane for you. You can get ARFoundation via the Unity Package Manager. It will work with many different types of devices.
You should start with a cheap Android phone or tablet, even if you own an iPhone, because it's easier to load the APK and debug/develop your app via Android build.
This is a simplification. I recommend using Singletons, ScriptableObjects, Object Pooling, and other Unity paradigms and plenty of other things within Unity that would help you but as another user pointed out - you may want to spend time learning Unity, ARFoundation, MapBox, and ask more specific programming questions when you are ready.
I'm trying to build a Remote Assistance solution using the Hololens 2 for university, i already set up the MRTK WebRTC example with Unity. Now i want to add the functionality of the desktop counterpart being able to add annotations in the field of view of the Hololens to support the remote guidance, but i have no idea how to achieve that. I was considering the Azure Spatial Anchors, but i haven't found a good example of adding 3D elements in the remote field of view of the Hololens from a 2D desktop environment. Also i'm not sure if the Spatial Anchors is the right framework, as they are mostly for persistent markers in the AR environment, and i'm rather looking for a temporary visual indicator.
Did anyone already work on such a solution and can give me a few frameworks/hints where to start?
To find the actual world location of a point from a 2D image, you can refer this answer: https://stackoverflow.com/a/63225342/11502506
In short, cameraToWorldMatrix and projectionMatrix transforms define for each pixel a ray in 3D space representing the path taken by the photons that produced the pixel. But anything along a certain ray will show up on the same pixel. So to find the actual world location of a point, you'll need either use Physics.Raycast method to calculate the impact point in world space where the ray hit the SpatialMapping.
I'm trying to use Vuforia in Unity to see a model in AR. It is working properly when I'm in a room with lost of different colors, but if I go in a room with one single color (example : white floor, white wall, no furniture), the model keeps disappearing. I'm using Extended tracking with Prediction enabled.
Is there a way to keep the model on screen whatever the background seen by webcam?
Is there a way to keep the model on screen whatever the background seen by webcam??
I am afraid this is not possible. Since vuforia uses Markerless Tracking it requires high contrast on the points.
Since most of AR SDKs only use a monocular RGB camera (not RGB-Depth), they rely on computer vision techniques to calculate missing depth information. It means extracting visual distinct feature points and locating device using estimated distance to these feature points over several frames while you move.
However, they also leverage from sensor fusion which means they combine data gathered from camera and the data from IMU unit(sensors) of the device. Unfortunately, this data is mainly used for complementing when motion tracking fails in situations like excessive motion(when camera image is blurred). Therefore, sensor data itself is not reliable which is the case when you walk into a room where there are no distinctive points to extract.
The only way you can solve this is by placing several image targets in that room. That will allow Vuforia to calculate device position in 3D space. Otherwise this is not possible.
You can also refer to SLAM for more information.
I'm working on a project for an exhibition where an AR scene is supposed to be layered on top of a 3D printed object. Visitors will be given a device with the application pre-installed. Various objects should be seen around / on top of the exhibit, so the precision of tracking is quite important.
We're using Unity to render the scene, this is not something that can be changed as we're already well into development. However, we're somewhat flexible on the technology we use to recognize the 3D object to position the AR camera.
So far we've been using Vuforia. The 3D target feature didn't scan our object very well, so we're resorting to printing 2D markers and placing them on the table that the exhibit sits on. The tracking is precise enough, the downside is that the scene disappears whenever the marker is lost, e.g. when the user tries to get a closer look at something.
Now we've recently gotten our hands on a Lenovo Phab 2 pro and are trying to figure out if Tango can improve on this solution. If I understand correctly, the advantage of Tango is that we can use its internal sensors and motion tracking to estimate its trajectory, so even when the marker is lost it will continue to render the scene very accurately, and then do some drift correction once the marker is reacquired. Unfortunately, I can't find any tutorials on how to localize the marker in the first place.
Has anyone used Tango for 3D marker tracking before? I had a look at the Area Learning example included in the Unity plugin, by letting it scan our exhibit and table in a mostly featureless room. It does recognize the object in the correct orientation even when it is moved to a different location, however the scene it always off by a few centimeters, which is not precise enough for our purposes. There is also a 2D marker detection API for Tango, but it looks like it only works with QR codes or AR tags (like this one), not arbitrary images like Vuforia.
Is what we're trying to achieve possible with Tango? Thanks in advance for any suggestions.
Option A) Sticking with Vuforia.
As Hristo points out, Your marker loss problem should be fixable with Extended Tracking. This sounds definitely worth testing.
Option B) Tango
Tango doesn't natively support other markers than the ARTags and QRCodes.
It also doesn't support the Area Learnt scene moving (much). If your 3DPrinted objects stayed stationary you could scan an ADF and should have good quality tracking. If all the objects stay still you should have a little but not too much drift.
However, if you are moving those 3D Printed objects, it will definitely throw that tracking off. So moving objects shouldn't be part of the scanned scene.
You could make an ADF Scan without the 3D objects present to track the users position, and then track the 3D printed objects with ARMarkers using Tangos ARMarker detection. (unsure - is that what you tried already?) . If that approach doesn't work, I think your only Tango option is to add more features/lighting etc.. to the space to make the tracking more solid.
Overall, Natural Feature tracking by Vuforia (or Marker tracking for robustness) sounds more suited to what I think your project is doing, as users will mostly be looking at the ARTag/NFT objects. However, if it's robustness is not up to scratch, Tango could provide a similar solution.