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RBE PhD Speaking Qualifier: Zhentian Qian | Semantic SLAM with Autonomous Object-Level Data Association


Various images of robots at Robotics Engineering WPI alt
WPI Robotics Engineering
Wednesday, May 12, 2021
10:00 am to 11:00 am


Zhentian Qian

Semantic SLAM with Autonomous Object-Level Data Association


Wednesday, May 12, 2021

10:00 AM - 11:00 AM

Virtual | Zoom:

Abstract: It is often desirable to capture and map semantic information of an environment during simultaneous localization and mapping (SLAM). Such semantic information can enable a robot to better distinguish places with similar low-level geometric and visual features and perform high-level tasks that use semantic information about objects to be manipulated and environments to be navigated. While semantic SLAM has gained increasing attention, there is little research on semantic-level data association based on semantic objects, i.e., object-level data association. In this paper, we propose a novel object-level data association algorithm based on bag of words algorithm, formulated as a maximum weighted bipartite matching problem. With object-level data association solved, we develop a quadratic-programming-based semantic object initialization scheme using dual quadric and introduce additional constraints to improve the success rate of object initialization. The integrated semantic-level SLAM system can achieve high-accuracy object-level data association and real-time semantic mapping as demonstrated in the experiments. The online semantic map building and semantic-level localization capabilities facilitate semantic-level mapping and task planning in a priori unknown environment.

Ph.D. Advisors:

Professor Jing Xiao, Worcester Polytechnic Institute (WPI)

Professor Jie Fu, Worcester Polytechnic Institute (WPI)

Ph.D. Qualifier Evaluators:

Professor Michael A. Gennert, Worcester Polytechnic Institute (WPI)

Professor Carlo Pinciroli, Worcester Polytechnic Institute (WPI)