147 citations · 236 across the 10 of their papers we have counts for
10 papers
UniCal: Unified Neural Sensor Calibration
Ze Yang, George Chen, Haowei Zhang +5
Self-driving vehicles (SDVs) require accurate calibration of LiDARs and cameras to fuse sensor data accurately for autonomy. Traditional calibration methods typically leverage fidu…
CADSim: Robust and Scalable in-the-wild 3D Reconstruction for Controllable Sensor Simulation
Jingkang Wang, Sivabalan Manivasagam, Yun Chen +5
Realistic simulation is key to enabling safe and scalable development of % self-driving vehicles. A core component is simulating the sensors so that the entire autonomy system can…
Deep Multi-Task Learning for Joint Localization, Perception, and Prediction
John Phillips, Julieta Martinez, Ioan Andrei Bârsan +3
Over the last few years, we have witnessed tremendous progress on many subtasks of autonomous driving, including perception, motion forecasting, and motion planning. However, these…
Asynchronous Multi-View SLAM
Anqi Joyce Yang, Can Cui, Ioan Andrei Bârsan +2
Existing multi-camera SLAM systems assume synchronized shutters for all cameras, which is often not the case in practice. In this work, we propose a generalized multi-camera SLAM f…
Learning to Localize Through Compressed Binary Maps
Xinkai Wei, Ioan Andrei Bârsan, Shenlong Wang +2
One of the main difficulties of scaling current localization systems to large environments is the on-board storage required for the maps. In this paper we propose to learn to compr…
Learning to Localize Using a LiDAR Intensity Map
Ioan Andrei Bârsan, Shenlong Wang, Andrei Pokrovsky +1
In this paper we propose a real-time, calibration-agnostic and effective localization system for self-driving cars. Our method learns to embed the online LiDAR sweeps and intensity…