diff --git a/android/app/README.md b/android/app/README.md index 5e4ded8a9..ccb007588 100644 --- a/android/app/README.md +++ b/android/app/README.md @@ -13,7 +13,7 @@ care is neccessary when using automated control (e.g. person following or drivin The app starts with a menu screen that shows all available screens. The settings screen can be opened with a click on the icon at the top right corner. By clicking on the other icons the user can access various screens whose functionalities are explained in the following.
- +
@@ -95,7 +95,7 @@ If a model is active, the inference speed in [ms] will be displayed next to the Free Roam offers simple robot control with real time updates and information about battery, speed and distance from surfaces.- +
- **Battery**: The battery icon shows realtime battery levels of the connected robot. @@ -119,7 +119,7 @@ Free Roam offers simple robot control with real time updates and information abo Simple UI for collection of data sets.- +
- **Preview Resolution**: Used to switch between resolutions of camera preview. There are 3 settings: @@ -140,7 +140,15 @@ Simple UI for collection of data sets. Simple UI to check the button and joystick mapping of a connected BT controller.- + +
+ +### Robot Info + +Simple UI to get robot info and test basic functionality. The **Robot Type** as configured in the firmware is displayed as text and animation. The checkmarks in the sections **Sensors**, **Wheel Odometry** and **LEDs** show which features are supported by the connected robot. The section **Readings** provides the most important sensor measurements. In the section **Send Commands**, users can send basic motor commands by pressing the corresponding buttons and control the front and rear LEDs with a slider. + ++
### Autopilot @@ -148,7 +156,7 @@ Simple UI to check the button and joystick mapping of a connected BT controller. Simple UI for running autopilot models.- +
### Object Tracking @@ -173,7 +181,7 @@ Note that this fragment requires ARCore and camera permission. If your device do All models are quantized for better performance on embedded devices. Please refer to section below for a short description of the available models and benchmarking results. The [mean Average Precision (mAP)](https://kharshit.github.io/blog/2019/09/20/evaluation-metrics-for-object-detection-and-segmentation) is computed on the validation set of the [COCO Detection 2017](https://cocodataset.org/#detection-2017) dataset. Each model is run for about 1 minute; the inference time is averaged across the last 100 frames and reported in frames per second (fps). Note that models with larger input resolution might be better for smaller objects despite lower mAP.- +
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