Extended Data Fig. 1: Performance on the RADIATE dataset, ablation study on the deep network, and run-time performance.
From: Deep learning-based robust positioning for all-weather autonomous driving
(a) To evaluate the generalization of GRAMME to new datasets, we train, validate and test models on the publicly available RADIATE dataset17 that contains shorter sequences for ego-motion estimation with high variation in scene and structure appearance. We report the mean depth predictions errors with standard deviations and the distribution of motion prediction errors with quartiles. Although GRAMME performs well in terms of depth prediction and ego-motion estimation performance, we observe on this dataset that due to dense fog and heavy precipitation, the performance of the lidar&camera-based GRAMME model drops significantly compared to the day sequences. (b) As an additional ablation study, we replace the UNet network in the GRAMME modules with commonly used networks. The results show the basis for our choice of ResNet in the GRAMME architecture. We also report the run-time requirements in milliseconds, indicating the real-time capability of GRAMME on a consumer-grade GPU.
