Gated and RGB Fusion for Robust Perception

Robust perception in all weather conditions is a critical requirement for autonomous vehicles. This project proposes fusing gated and conventional RGB camera images for robust scenes encoding, depth estimation and trajectory prediction. Conventional approaches using lidar and RGB camera fail to perform robustly in rain, fog and snow. By extending existing computer vision algorithms to Gated-RGB camera pair the fusion algorithms developed will utilize features that are robust in one sensor modality but not the other. The proposed projects will allow Algolux to evaluate different fusion algorithms and augment the existing captured dataset with simulated data that are useful for algorithm development and evaluation.

Faculty Supervisor:

Michael Langer

Student:

Yiran Mao;Amanpreet Walia

Partner:

Algolux

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

McGill University