We suggest to create a new conda environment with all required packages. Results on synthetic and on real-world data show superiority over other approaches. We propose a deep learning-based framework for learning an OGM algorithm which is both capable of quantifying uncertainty and which does not rely on manually labeled data. Deep learning-based ISMs face the challenge of limited training data and they often cannot handle uncertainty quantification yet. Geometric ISMs show a limited performance when estimating states in unobserved but inferable areas and have difficulties dealing with ambiguous input. Inverse sensor models (ISMs) are used to compute OGMs from sensor data such as lidar point clouds. Abstract - Evidential occupancy grid maps (OGMs) are a popular representation of the environment of automated vehicles.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. Archives
June 2023
Categories |