Search Results for author:
Found 11 papers, 2 papers with code
UniCal: Unified Neural Sensor Calibration
We jointly learn the sensor calibration and the underlying scene representation through differentiable volume rendering, utilizing outdoor sensor data without the need for specific calibration fiducials.
CADSim: Robust and Scalable in-the-wild 3D Reconstruction for Controllable Sensor Simulation
To tackle these issues, we present CADSim, which combines part-aware object-class priors via a small set of CAD models with differentiable rendering to automatically reconstruct vehicle geometry, including articulated wheels, with high-quality appearance.
Towards Zero Domain Gap: A Comprehensive Study of Realistic LiDAR Simulation for Autonomy Testing
We leverage this setting to analyze what aspects of LiDAR simulation, such as pulse phenomena, scanning effects, and asset quality, affect the domain gap with respect to the autonomy system, including perception, prediction, and motion planning, and analyze how modifications to the simulated LiDAR influence each part.
Deep Multi-Task Learning for Joint Localization, Perception, and Prediction
Over the last few years, we have witnessed tremendous progress on many subtasks of autonomous driving, including perception, motion forecasting, and motion planning.
Asynchronous Multi-View SLAM
Existing multi-camera SLAM systems assume synchronized shutters for all cameras, which is often not the case in practice.
Pit30M: A Benchmark for Global Localization in the Age of Self-Driving Cars
We are interested in understanding whether retrieval-based localization approaches are good enough in the context of self-driving vehicles.
Learning to Localize Through Compressed Binary Maps
One of the main difficulties of scaling current localization systems to large environments is the on-board storage required for the maps.
Learning to Localize Using a LiDAR Intensity Map
In this paper we propose a real-time, calibration-agnostic and effective localization system for self-driving cars.
Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networks
Compressing large neural networks is an important step for their deployment in resource-constrained computational platforms.
Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization
In this paper we propose a novel semantic localization algorithm that exploits multiple sensors and has precision on the order of a few centimeters.
Robust Dense Mapping for Large-Scale Dynamic Environments
We use both instance-aware semantic segmentation and sparse scene flow to classify objects as either background, moving, or potentially moving, thereby ensuring that the system is able to model objects with the potential to transition from static to dynamic, such as parked cars.
