Scalable Localization and Mapping For Autonomous Navigation

PhD Thesis Proposal Defence


Title: "Scalable Localization and Mapping For Autonomous Navigation"

by

Mr. Huaiyang HUANG


Abstract:

State estimation is a crucial building block for an autonomous navigation 
system with high intelligence. Techniques for state estimation enable an 
agent to perceive geometric information related to itself and its 
surroundings, e.g., sensor pose tracking, self-localization, and mapping. 
For run-time robustness and precision, generally, robots are equipped with 
heterogeneous sensors to guarantee necessary information redundancy, which 
requires the state estimation module to fully exploit the advantages of 
individual sensors while fusing the perceptual data in a complementary 
manner.

This proposal presents a scalable state estimation system aided by dense 
mapping and cross-modal visual localization. We begin with a 
bundle-adjusted point cloud mapping method that provides the dense 
geometric structure for the localization system. Then, we explore the 
cross-modal localization method as an intermediate way of combining the 
advantages of visual and geometric measurements. The basic idea is to 
associate sparse visual structure with pre-built geometric structure, and 
then introduce the structure regulation in the optimization to eliminate 
the drift and align the coordinate globally. Finally, based on the above 
contents, we further discuss the remaining research problems and possible 
solutions.


Date:			Wednesday, 13 April 2022

Time:                  	3:00pm - 5:00pm

Zoom Meeting: 
https://hkust.zoom.us/j/93660424101?pwd=ejBDT2E0VnpFakZFOFdxb0hwRU5lZz09

Committee Members:	Dr. Ming Liu (Supervisor)
  			Dr. Qifeng Chen (Chairperson)
 			Dr. Sai-Kit Yeung
 			Dr. Lei Zhu (ROAS Thrust)


**** ALL are Welcome ****