Daniela L. Rus — accepted AI-conference papers

2023

BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation

Zhijian Liu, Haotian Tang, Alexander Amini, Xinyu Yang, Huizi Mao, Daniela L. Rus +1

ICRA 2023poster

Multi-sensor fusion is essential for an accurate and reliable autonomous driving system. Recent approaches are based on point-level fusion: augmenting the LiDAR point cloud with camera features. However, the camera-to-LiDAR projection throws away the semantic density of camera features, hindering th…

Cited by 1130 Source ↗code ↗Scholar ↗

2021

Efficient and Robust LiDAR-Based End-to-End Navigation

Zhijian Liu, Alexander Amini, Sibo Zhu, Sertac Karaman, Song Han, Daniela L. Rus

ICRA 2021poster

Deep learning has been used to demonstrate end-to-end neural network learning for autonomous vehicle control from raw sensory input. While LiDAR sensors provide reliably accurate information, existing end-to-end driving solutions are mainly based on cameras since processing 3D data requires a large…

Cited by 56 Source ↗Scholar ↗