To Sparsify or not to Sparsify: Simplifying Visual Feature Maps for Mobile Agents

Real-time pose estimation is crucial for autonomous agents for motion control and navigation, and it is essential for extended reality applications as well. As the current global positioning systems are not reliable indoors or are not precise enough, visual simultaneous localization and mapping is becoming prevalent in autonomous agents and mobile devices. The visual feature maps can be shared between agents and might be merged into a common map on a server and redistributed to all clients to enable re-localization and co-localization. However, merged maps can grow continuously in density and size that constrained agents are not able to handle anymore.In this paper, we investigate how the map density effects localization and visual odometry performance on low-cost mobile clients. We show that a sparse representation of the original VFM is necessary for real-time visual odometry but at the cost of a degraded re-localization performance.