microsoft · jonyMarino · Mar 4, 2021 · Dec 21, 2020 · Dec 22, 2020 · Dec 22, 2020
diff --git a/docs/event_sim.md b/docs/event_sim.md
@@ -0,0 +1,55 @@
+AirSim provides a Python-based event camera simulator, aimed at performance and ability to run in real-time along with the sim.
+
+#### Event cameras
+An event camera is a special vision sensor that measures changes in logarithmic brightness and only reports 'events'. Each event is a set of four values that gets generated every time the absolute change in the logarithmic brightness exceeds a certain threshold. An event contains the timestamp of the measurement, pixel location (x and y coordinates) and the polarity: which is either +1/-1 based on whether the logarithmic brightness has increased or decreased. Most event cameras have a temporal resolution of the order of microseconds, making them significantly faster than RGB sensors, and also demonstrate a high dynamic range and low motion blur. More details about event cameras can be found in [this tutorial from RPG-UZH](http://rpg.ifi.uzh.ch/docs/scaramuzza/Tutorial_on_Event_Cameras_Scaramuzza.pdf)
+
+#### AirSim event simulator
+
+The AirSim event simulator uses two consecutive RGB images (converted to grayscale), and computes "past events" that would have occurred during the transition based on the change in log luminance between the images. These events are reported as a stream of bytes, following this format:
+
+`<x> <y> <timestamp> <pol>`
+
+x and y are the pixel locations of the event firing, timestamp is the global timestamp in microseconds and pol is either +1/-1 depending on whether the brightness increased or decreased. Along with this bytestream, an accumulation of events over a 2D frame is also constructed, known as an 'event image' that visualizes +1 events as red and -1 as blue pixels. An example event image is shown below:
+
+![image](images/event_sim.png)
+
+#### Usage
+An example script to run the event simulator alongside AirSim is located at https://github.com/microsoft/AirSim/blob/master/PythonClient/eventcamera_sim/test_event_sim.py. The following optional command-line arguments can be passed to this script.
+
+```
+args.width, args.height (float): Simulated event camera resolution
+args.save (bool): Whether or not to save the event data to a file, args.debug (bool): Whether or not to display the simulated events as an image
+```
+
+The implementation of the actual event simulation, written in Python and numba, is at https://github.com/microsoft/AirSim/blob/master/PythonClient/eventcamera_sim/event_simulator.py. The event simulator is initialized as follows, with the arguments controlling the resolution of the camera.
+
+```
+from event_simulator import *
+ev_sim = EventSimulator(W, H)
+```
+
+The actual computation of the events is triggered through an `image_callback` function, which is executed every time a new RGB image is obtained. The first time this function is called, due to the lack of a 'previous' image, it acts as an initialization of the event sim. 
+
+```
+event_img, events = ev_sim.image_callback(img, ts_delta)
+```
+This function, which behaves similar to a callback (called every time a new image is received) returns an event image as a one dimensional array of +1/-1 values, thus indicating only whether events were seen at each pixel, but not the timing/number of events. This one dimensional array can be converted into the red/blue event image as seen in the function `convert_event_img_rgb`. `events` is a numpy array of events, each of format `<x> <y> <timestamp> <pol>`.
+
+Through this function, the event sim computes the difference between the past and the current image, and computes a stream of events which is then returned as a numpy array. This can then be appended to a file.
+
+There are quite a few parameters that can be tuned to achieve a level of visual fidelity/performance of the event simulation. The main factors to tune are the following:
+
+1. The resolution of the camera.
+2. The log luminance threshold `TOL` that determines whether or not a detected change counts as an event.
+
+Note: There is also currently a max limit on the number of events generated per pair of images, which can also be tuned.
+
+
+#### Algorithm
+The working of the event simulator loosely follows this set of operations:
+1. Take the difference between the log intensities of the current and previous frames.  
+2. Iterating over all pixels, calculate the polarity for each each pixel based on a threshold of change in log intensity.  
+3. Determine the number of events to be fired per pixel, based on extent of intensity change over the threshold. Let $N_{max}$ be the maximum number of events that can occur at a single pixel, then the total number of firings to be simulated at pixel location $u$ would be $N_e(u) = min(N_{max}, \frac{\Delta L(u)}{TOL})$.  
+4. Determine the timestamps for each interpolated event by interpolating between the amount of time that has elapsed between the captures of the previous and current images.  
+$t = t_{prev} + \frac{\Delta T}{N_e(u)}$  
+5. Generate the output bytestream by simulating events at every pixel and sort by timestamp.
diff --git a/docs/images/event_sim.png b/docs/images/event_sim.png
diff --git a/docs/images/octomap.png b/docs/images/octomap.png
diff --git a/docs/images/voxel_grid.png b/docs/images/voxel_grid.png
diff --git a/docs/voxel_grid.md b/docs/voxel_grid.md
@@ -0,0 +1,55 @@
+AirSim provides a feature that constructs ground truth voxel grids of the world directly from Unreal Engine. A voxel grid is a representation of the occupancy of a given world/map, by discretizing into cells of a certain size; and recording a voxel if that particular location is occupied. 
+
+The logic for constructing the voxel grid is in WorldSimApi.cpp->createVoxelGrid(). For now, the assumption is that the voxel grid is a cube - and the API call from Python is of the structure:
+
+```
+simCreateVoxelGrid(self, position, x, y, z, res, of)
+
+position (Vector3r): Global position around which voxel grid is centered in m
+x, y, z (float): Size of each voxel grid dimension in m
+res (float): Resolution of voxel grid in m
+of (str): Name of output file to save voxel grid as
+```
+
+Within `createVoxelGrid()`, the main Unreal Engine function that returns occupancy is [OverlapBlockingTestByChannel](https://docs.unrealengine.com/en-US/API/Runtime/Engine/Engine/UWorld/OverlapBlockingTestByChannel/index.html).
+
+```
+OverlapBlockingTestByChannel(position, rotation, ECollisionChannel, FCollisionShape, params);
+```
+
+This function is called on the positions of all the 'cells' we wish to discretize the map into, and the returned occupancy result is collected into an array `voxel_grid_`. The indexing of the cell occupancy values follows the convention of the [binvox](https://www.patrickmin.com/binvox/binvox.html) format. 
+
+```
+for (float i = 0; i < ncells_x; i++) {
+    for (float k = 0; k < ncells_z; k++) {
+        for (float j = 0; j < ncells_y; j++) {
+            int idx = i + ncells_x * (k + ncells_z * j);
+            FVector position = FVector((i - ncells_x /2) * scale_cm, (j - ncells_y /2) * scale_cm, (k - ncells_z /2) * scale_cm) + position_in_UE_frame;
+            voxel_grid_[idx] = simmode_->GetWorld()->OverlapBlockingTestByChannel(position, FQuat::Identity, ECollisionChannel::ECC_Pawn, FCollisionShape::MakeBox(FVector(scale_cm /2)), params);
+        }
+    }
+}
+```
+
+The occupancy of the map is calculated iteratively over all discretized cells, which can make it an intensive operation depending on the resolution of the cells, and the total size of the area being measured. If the user's map of interest does not change much, it is possible to run the voxel grid operation once on this map, and save the voxel grid and reuse it. For performance, or with dynamic environments, we recommend running the voxel grid generation for a small area around the robot; and subsequently use it for local planning purposes.
+
+
+The voxel grids are stored in the binvox format which can then be converted by the user into an octomap .bt or any other relevant, desired format. Subsequently, these voxel grids/octomaps can be used within mapping/planning. One nifty little utility to visualize a created binvox files is [viewvox](https://www.patrickmin.com/viewvox/). Similarly, `binvox2bt` can convert the binvox to an octomap file.
+
+##### Example voxel grid in Blocks:
+![image](images/voxel_grid.png)
+
+##### Blocks voxel grid converted to Octomap format (visualized in rviz):
+![image](images/octomap.png)
+
+As an example, a voxel grid can be constructed as follows, once the Blocks environment is up and running:
+
+```
+import airsim
+c = airsim.VehicleClient()
+center = airsim.Vector3r(0, 0, 0)
+output_path = os.path.join(os.getcwd(), "map.binvox")
+c.simCreateVoxelGrid(center, 100, 100, 100, 0.5, output_path)
+```
+
+And visualized through `viewvox map.binvox`.
diff --git a/mkdocs.yml b/mkdocs.yml
@@ -6,6 +6,8 @@ site_description: 'Open source simulator based on Unreal Engine for autonomous v
 markdown_extensions:
     - toc:
         permalink: "#"
+    - pymdownx.arithmatex:
+        generic: true
     - pymdownx.extra:
 
 remote_branch: gh-pages
@@ -27,6 +29,11 @@ extra:
       - type: github-alt
         link: https://github.com/Microsoft/AirSim
 
+extra_javascript:
+  - javascripts/config.js
+  - https://polyfill.io/v3/polyfill.min.js?features=es6
+  - https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js
+
 copyright: Copyright &copy; 2018 Microsoft Research
 
 nav:
@@ -64,6 +71,8 @@ nav:
     - "Domain Randomization": "retexturing.md"
     - "Mesh Vertex Buffers": "meshes.md"
     - "Playing Logs": 'playback.md'
+    - "Voxel Grid Generator": "voxel_grid.md"
+    - "Event camera" : "event_sim.md"
   - "Design":
     - "Architecture": 'design.md'
     - "Code Structure": 'code_structure.md'