Autonomous Vehicle Perception and Navigation in Adverse Conditions

Alvari Seppänen

Research output: ThesisDoctoral ThesisCollection of Articles

Abstract

Autonomous mobility has gained popularity in recent years due to the promise of safer and more efficient transportation systems. However, multiple challenges hinder the realization of fully autonomous transportation, e.g., safety, operational environment limitations, and efficiency. This thesis addresses challenges related to the perception and navigation of outdoor mobile robots in adverse conditions. These conditions refer to adverse weather and limited communication between a remote operator and the robot. Adverse weather conditions affect the perception systems, namely light detection and ranging (LiDAR) sensors, causing specific types of noise to the data. This work aims to denoise this data and thus provide clean data for downstream systems. Two deep-learning-based denoising approaches are proposed: a supervised approach that utilizes a spatiotemporal module and a selfsupervised multi-echo approach. The supervised method's spatiotemporal module enables efficient data usage and generalization from semi-synthetic to fully real-world data. The self-supervised approach learns by predicting the correlation of data points to their neighbors and utilizes multiecho point clouds for recovering the points representing solid objects. Experiments show that both approaches achieved state-of-the-art performance. Another challenge addressed in this thesis is the navigation in adverse conditions. These challenges refer to limited remote communication caused, for example, by adverse weather conditions. The limited communication between teleoperators and semi-autonomous mobile robots is studied. Semi-autonomous control strategies are proposed to aid the teleoperators when communication signal limits the system's performance. Experiments with a mobile robot prototype revealed that the strategies improved the navigation. Future research should focus on testing the denoising with downstream algorithms and assessing the control strategies in more complex environments. Many adverse and unexpected scenarios must be addressed to realize fully autonomous vehicles in complex environments. Therefore, more unified solutions tackling multiple issues simultaneously are desired in future research.
Translated title of the contributionAutonomisen ajoneuvon havainnointi ja liikkuvuus epäsuotuisissa olosuhteissa
Original languageEnglish
QualificationDoctor's degree
Awarding Institution
  • Aalto University
Supervisors/Advisors
  • Tammi, Kari, Supervising Professor
Publisher
Print ISBNs978-952-64-1714-1
Electronic ISBNs978-952-64-1715-8
Publication statusPublished - 2024
MoE publication typeG5 Doctoral dissertation (article)

Keywords

  • autonomous vehicle
  • adverse conditions
  • computer vision
  • deep learning

Fingerprint

Dive into the research topics of 'Autonomous Vehicle Perception and Navigation in Adverse Conditions'. Together they form a unique fingerprint.

Cite this