Ioan Andrei Bârsan
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View article: UniCal: Unified Neural Sensor Calibration
UniCal: Unified Neural Sensor Calibration Open
Self-driving vehicles (SDVs) require accurate calibration of LiDARs and cameras to fuse sensor data accurately for autonomy. Traditional calibration methods typically leverage fiducials captured in a controlled and structured scene and com…
View article: CADSim: Robust and Scalable in-the-wild 3D Reconstruction for Controllable Sensor Simulation
CADSim: Robust and Scalable in-the-wild 3D Reconstruction for Controllable Sensor Simulation Open
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 be tested in simulation. Sensor simulation involves modelin…
Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networks Open
Compressing large neural networks is an important step for their deployment in resource-constrained computational platforms. In this context, vector quantization is an appealing framework that expresses multiple parameters using a single c…
Deep Multi-Task Learning for Joint Localization, Perception, and Prediction Open
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 systems often assume that the car is accurately localized …
Asynchronous Multi-View SLAM Open
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 formulation which accounts for asynchronous sensor observati…
Deep Multi-Task Learning for Joint Localization, Perception, and\n Prediction Open
Over the last few years, we have witnessed tremendous progress on many\nsubtasks of autonomous driving, including perception, motion forecasting, and\nmotion planning. However, these systems often assume that the car is accurately\nlocaliz…
Learning to Localize Through Compressed Binary Maps Open
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 compress the map representation such that it is optimal for the …
Learning to Localize Using a LiDAR Intensity Map Open
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 map into a joint deep embedding space. Localization is the…
Pit30M: A Benchmark for Global Localization in the Age of Self-Driving Cars Open
We are interested in understanding whether retrieval-based localization approaches are good enough in the context of self-driving vehicles. Towards this goal, we introduce Pit30M, a new image and LiDAR dataset with over 30 million frames, …
Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization Open
In this paper we propose a novel semantic localization algorithm that exploits multiple sensors and has precision on the order of a few centimeters. Our approach does not require detailed knowledge about the appearance of the world, and ou…
Robust Dense Mapping for Large-Scale Dynamic Environments Open
We present a stereo-based dense mapping algorithm for large-scale dynamic\nurban environments. In contrast to other existing methods, we simultaneously\nreconstruct the static background, the moving objects, and the potentially\nmoving but…