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Found 5 papers, 1 papers with code
Privacy Preserving Structure-from-Motion
The recent trend towards cloud-based localization and mapping systems has raised significant privacy concerns.
Privacy Preserving Partial Localization
We propose a principled approach overcoming these limitations, based on two observations.
Privacy Preserving Localization and Mapping From Uncalibrated Cameras
In this paper, we propose a solution to the uncalibrated privacy preserving localization and mapping problem.
Infrastructure-based Multi-Camera Calibration using Radial Projections
In particular, our approach is more robust than the naive approach of first estimating intrinsic parameters and pose per camera before refining the extrinsic parameters of the system.
Efficient 2D-3D Matching for Multi-Camera Visual Localization
This results in a system that provides reliable and drift-less pose estimations for high speed autonomous driving.
Robotics
