Introspective learning based Visual-LiDAR fusion for adaptive Simultaneous Localization and Mapping
Kedia, Shubham
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https://hdl.handle.net/2142/121406
Description
Title
Introspective learning based Visual-LiDAR fusion for adaptive Simultaneous Localization and Mapping
Author(s)
Kedia, Shubham
Issue Date
2023-06-08
Director of Research (if dissertation) or Advisor (if thesis)
Hauser, Kris
Department of Study
Mechanical Sci & Engineering
Discipline
Mechanical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Slam
Computer Vision
Sensor Fusion
State Estimation
Deep Learning
Robotics
Optimization
Adaptive Slam
Language
eng
Abstract
This work is about developing an adaptive Visual-LiDAR Simultaneous Localization and Mapping (SLAM) algorithm. The objective is to develop a SLAM system that can adaptively negotiate LiDAR degenerate scenarios and visually challenging environments using sensor fusion. The fusion is based on the Pose Graph Optimization (PGO) technique, utilizing adaptive fusion weights predicted from a Deep Neural Network (DNN) model. The DNN model framework is inspired by introspective learning for vision systems. The DNN model is trained on a large dataset called TartanAir, which has diverse and challenging environmental conditions. The output of the model is the predicted error on the visual odometry and LiDAR odometry, which is used to compose the information matrix of the PGO. The PGO framework with weighted pose constraints from visual odometry, LiDAR odometry, and loop closure is solved using the Levenberg–Marquardt optimization algorithm. The proposed framework shows superior performance compared to the visual-only, LiDAR-only SLAM, and baseline fusion methods that were evaluated in this study.
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