Table of Contents
- Overview of Render Settings
- Overview of Background Settings
- Overview of Perception Settings
Anytime data is being drawn visualized, the render settings are made available. The render settings provide a means of augmenting the appearance of the visualized data. Some settings are intuitive while others require a little explanation. These settings can be powerful tools to help highlight attributes of the data or visually separate different scans.
Here is a breakdown of the render settings:
|Point Size Scale||Changes the scale of the sensor reading data points to make them appear bigger or smaller.|
|Min Point Size (in pixels)||The minimum point size (in pixels) that a point will be drawn. No points will be drawn smaller than this. Used to prevent data points from disappearing when they are far away and the current point size scale setting would render them smaller than a pixel.|
|Color Scheme||Changes the color scheme used to color the sensor reading data points. Color schemes can be uniform (ie: solid red) or attribute dependent. For example, the color scheme "Gradient on Radial Distance" will color the data points based on their radial distance from the sensor.|
|Color Map||The color map used to color sensor readings on a gradient. This setting only applies if the color scheme requires a gradient.|
|Legend Saturation Limits||Adjusts the limits of the color map gradient such that the gradient is completely saturated at the limits. Suppose you set the "Color Scheme" to be a gradient on signal strength, and that you set the "Color Map" to be grayscale. Then adjusting the saturation limits to something like 100 and 200, would cause any sensor reading with a signal strength below 100 to be colored black and any reading with a signal strength above 200 to be colored white. In the range 100 to 200 the sensor readings would be colored on a gradient between black and white. It is most useful to adjust this in real-time, while looking at the data and the legend.|
|Sweep Decay History||Toggle for visualizing previous sweeps as an overlaid history. Note that this will increase the processing requirements of the visualizer.|
|Count||Change the quantity of historical sweeps that will be drawn.|
|Translucency||Change the presence or type of translucency scheme to better differentiate historical sweeps.|
The background settings provide a means to adjust the aesthetics of the visualizer.
|Background Color||Changes the color of the visualizer background.|
|Grid Color||Changes the color of the visualizer grid.|
|Grid Bounds||Changes the maximum bounds of the grid (ie: 1500 means a Cartesian grid extending from -1500:1500 in both x and y, and a polar grid extending to a radius of 1500).|
|Cartesian Grid||The toggle switch toggles the presence of the Cartesian grid. The input box permits the user to change the size of a Cartesian grid square.|
|Polar Grid||The toggle switch toggles the presence of the polar grid. The input box permits the user to change the distance between radial delineations on the polar grid.|
Eventually the SDK might provide numerous robust methods/solutions to common perception tasks. However these features were put on hold until engineering resources free up to incorporate them in future releases of the software. Currently the SWEEP VISUALIZER provides some experimental prototypes of basic boundary detection via line-extraction, and basic Cartesian clustering. These features are built into the visualizer in an interpreted language, and are not optimized. They were meant to be an experiment with including perception features in the Visualizer, but do NOT reflect robust implementations. Nonetheless, have fun experimenting with them!
Boundary Detection Settings
The boundary detection settings provide a means to tune the line-extraction algorithm (split-merge).
|Toggle Detection||Toggles the boundary detection on or off.|
|Min Radius||Defines a minimum radius for points to be considered in the line extraction. This prevents lines from forming on sensor readings near the sensor.|
|Distance Threshold||The max distance between a point and a line at which a point is to be considered a part of that line.|
|Collinearity Threshold||A metric for how collinear two lines segments have to be before they are merged into a single segment. Lower values are more strict, that is the segments must be more collinear in order to be merged. Higher values will attempt to merge lines that aren't as collinear.|
|Min Num Pts in Line||The minimum number of points for a line to remain. Lines made up of fewer points will be removed. This is the only currently implemented post-processing technique.|
|Use Intermediate Line Fitting||Whether or not to use intermediate line fitting during the split portion of the split-merge line extraction algorithm. If true, the recursive split step will consider the most distant point to the line that is formed as a fit to a subset of points. If false, the recursive split step will consider the most distant point to the line that runs between the two most distant points in a subset of points.|
The clustering settings provide a means to tune the basic clustering methods. Currently only common Cartesian based clustering algorithms are provided.
|Toggle Clustering||Toggles clustering on or off.|
|Clustering Method||Selects a method for clustering.|
|Neighborhood Radius||The size of the neighborhood radius in cm.|
|Minimum Cluster Size||The number of points in a radius to form a cluster.|
|Number of Clusters||The desired number of clusters (for k-means only)|