Deep Sensor Fusion between 2D Laser Scanner and IMU for Mobile Robot Localization

Chi Li, Sen Wang, Yan Zhuang, Fei Yan

Research output: Contribution to journalArticlepeer-review

46 Citations (Scopus)
971 Downloads (Pure)


Multi-sensor fusion plays a key role in 2D laser-based robot location and navigation. Although it has achieved great success, there are still some challenges, e.g., being fragile when having a large angular rotation. In this paper, we present a deep learning-based approach to localizing a mobile robot using a 2D laser and an inertial measurement unit. A novel recurrent convolutional neural network (RCNN)-based architecture is developed to fuse laser and inertial data for scan-to-scan pose estimation. A scan-to-submap optimization is also introduced to optimize the poses estimated by the RCNN for enhanced robustness and accuracy. Extensive experiments have been conducted in both simulation and practice with a real mobile robot, verifying the effectiveness of the proposed deep sensor fusion system.

Original languageEnglish
Pages (from-to)8501-8509
Number of pages9
JournalIEEE Sensors Journal
Issue number6
Early online date12 Apr 2019
Publication statusPublished - 15 Mar 2021


  • 2D laser scanning
  • Data fusion
  • inertial measurement unit (IMU)
  • pose estimation

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering


Dive into the research topics of 'Deep Sensor Fusion between 2D Laser Scanner and IMU for Mobile Robot Localization'. Together they form a unique fingerprint.

Cite this