Probabilistic Uncertainty Quantification of Prediction Models with Application to Visual Localization
Junan Chen,Josephine Monica,Wei-Lun Chao,Mark Campbell,Junan Chen,Josephine Monica,Wei-Lun Chao,Mark Campbell
The uncertainty quantification of prediction models (e.g., neural networks) is crucial for their adoption in many robotics applications. This is arguably as important as making accurate predictions, especially for safety-critical applications such as self-driving cars. This paper proposes our approach to uncertainty quantification in the context of visual localization for autonomous driving, where...
