An RViz plugin for displaying satellite maps and aerial imagery based on GPS coordinates.
rviz_satellite is a ROS2 RViz plugin that displays satellite imagery and aerial maps based on GPS coordinates. It fetches map tiles from online tile servers or local files and overlays them in RViz visualization, allowing robotics developers to see their robot's position in real-world geographic context. The plugin supports standard OpenStreetMap tile conventions and various commercial tile providers.
ROS2 developers and robotics engineers who need to visualize robot positions with real-world satellite imagery in their RViz environments. Particularly useful for outdoor robotics applications like autonomous vehicles, drones, and field robots.
Developers choose rviz_satellite because it provides seamless integration of geographic mapping into ROS2 visualization workflows, supports multiple tile sources including local offline maps, and offers flexible configuration options for different mapping scenarios without requiring complex external mapping solutions.
Display internet satellite imagery in RViz
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Directly subscribes to sensor_msgs/msg/NavSatFix topics to display satellite maps at the robot's current position, providing immediate geographic context in RViz.
Supports various tile servers like OpenStreetMap, TomTom, and Mapbox with custom URL configurations, allowing integration with commercial or region-specific mapping services.
Can load tiles from local filesystems using file URIs and configure region-specific maps with custom coordinate reference systems, enabling use in disconnected environments.
Caches map tiles locally to $HOME/.cache/rviz_satellite, reducing repeated network requests and improving load times for frequently accessed areas.
The cache does not expire automatically; users must manually delete files to reload tiles, as stated in the README, which can lead to stale data if not managed.
Only supports raster tiles per the OpenStreetMap convention, excluding vector tiles that offer better scalability, dynamic styling, and lower bandwidth usage.
Configuring local maps requires precise parameters like meter per pixel and origin CRS, which can be error-prone and challenging for users without GIS expertise.