Title: Deep Learning for Building and Validating Geometric and Semantic Maps

Date: Monday, March 21, 2022
Time: 4 pm (EDT)
Location (Virtual): https://gatech.zoom.us/j/99561135585?pwd=VDFCREYyeDNMTEN5RC9OREVCREhlQT09

John Lambert
Ph.D. Student in Computer Science
School of Interactive Computing, College of Computing
Georgia Institute of Technology

Committee:
Dr. James Hays (Co-Advisor), School of Interactive Computing, Georgia Institute of Technology
Dr. Frank Dellaert (Co-Advisor), School of Interactive Computing, Georgia Institute of Technology
Dr. Zsolt Kira, School of Interactive Computing, Georgia Institute of Technology
Dr. Cedric Pradalier, School of Interactive Computing, Georgia Institute of Technology, Lorraine
Dr. Simon Lucey, Australian Institute for Machine Learning, University of Adelaide

Abstract:

Mapping the world is an essential tool for making spatial artificial intelligence a reality in our near future. Spatial AI, or embodied intelligence for 3D perception, enables awareness and understanding of our surroundings. Current methods for building and validating geometric and semantic maps are limited in several ways. For example, floorplan maps constructed from sparse camera views within indoor environments generally suffer from low completeness. In other domains, such as city streets, the world is ever-changing, making online validation of high-definition (HD) maps a requirement for today's self-driving vehicles; however, many current methods suffer from high-storage costs or limited accuracy.

In this dissertation, I first develop a new learning-based approach, SALVe, for creating complete and accurate 2d geometric maps (floorplans) under very wide baselines and occlusion. Second, I explore the role of the deep "front end" in Structure-from-Motion (SfM), and analyze its use in a new system for global SfM, GTSFM. Finally, I introduce learning-based formulations for solving the HD map change detection task in a bird’s eye view and ego-view. We collect the first public dataset for the task, which we entitle the Trust, but Verify (TbV) dataset, by mining thousands of hours of data from over 9 months of autonomous vehicle fleet operations. Because real map changes are infrequent and vector maps are easy to synthetically manipulate, we lean on simulated data to train such models. Perhaps surprisingly, we show that such models can generalize to real world distributions.