University of Illinois Urbana-Champaign

Introspective learning based Visual-LiDAR fusion for adaptive Simultaneous Localization and Mapping

Kedia, Shubham

Content Files
KEDIA-THESIS-2023.pdf

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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
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.
Graduation Semester
2023-08
Type of Resource
Thesis
Handle URL
https://hdl.handle.net/2142/121406
Copyright and License Information
Copyright 2023 Shubham Kedia

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