Tobias Riedlinger
Papers
1
Total Citations
2
H-Index
1
About
Tobias Riedlinger is a researcher advancing the field of autonomous perception, with a focus on efficient and reliable object detection in LiDAR point clouds. His key contributions center on developing lightweight, real-time methods for predicting the quality of detection outputs, enabling safer and more robust autonomous systems. His most notable work, "LMD: Light-Weight Prediction Quality Estimation for Object Detection in Lidar Point Clouds" (2024), introduces a novel approach to estimating detection confidence without heavy computational overhead, a critical step for deploying self-driving technology in resource-constrained environments. Though early in its trajectory, this paper has already garnered 2 citations, signaling its relevance to the community. Riedlinger’s research addresses the pressing need for uncertainty quantification in perception models, bridging the gap between high-performance detection and practical, deployable safety mechanisms. His work is particularly valuable for students and engineers seeking to understand how to build more trustworthy autonomous systems through efficient quality estimation, making him a rising voice in the intersection of computer vision and robotics.
Research Focus
Key Achievements
Top Papers
- 1