From SLAM to Situational Awareness: Challenges and Survey

The capability of a mobile robot to efficiently and safely perform complex missions is limited by its knowledge of the environment, namely the . Advanced reasoning, decision-making, and execution skills enable an intelligent agent to act autonomously in unknown environments. Situational Awareness (S...

Full description

Saved in:
Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-05, Vol.23 (10), p.4849
Main Authors: Bavle, Hriday, Sanchez-Lopez, Jose Luis, Cimarelli, Claudio, Tourani, Ali, Voos, Holger
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Awareness
Bridge maintenance
Citation indexes
Decision making
Global positioning systems
GPS
Humans
Intelligence
Intelligent agents
Localization
Magnetic fields
mobile robots
Multisensor fusion
Review
Robotics
Robotics industry
Robots
scene graphs
scene understanding
Semantics
Sensors
Simultaneous localization and mapping
Situational awareness
State estimation
Surveys
Unknown environments
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The capability of a mobile robot to efficiently and safely perform complex missions is limited by its knowledge of the environment, namely the . Advanced reasoning, decision-making, and execution skills enable an intelligent agent to act autonomously in unknown environments. Situational Awareness (SA) is a fundamental capability of humans that has been deeply studied in various fields, such as psychology, military, aerospace, and education. Nevertheless, it has yet to be considered in robotics, which has focused on single compartmentalized concepts such as sensing, spatial perception, sensor fusion, state estimation, and Simultaneous Localization and Mapping (SLAM). Hence, the present research aims to connect the broad multidisciplinary existing knowledge to pave the way for a complete SA system for mobile robotics that we deem paramount for autonomy. To this aim, we define the principal components to structure a robotic SA and their area of competence. Accordingly, this paper investigates each aspect of SA, surveying the state-of-the-art robotics algorithms that cover them, and discusses their current limitations. Remarkably, essential aspects of SA are still immature since the current algorithmic development restricts their performance to only specific environments. Nevertheless, Artificial Intelligence (AI), particularly Deep Learning (DL), has brought new methods to bridge the gap that maintains these fields apart from the deployment to real-world scenarios. Furthermore, an opportunity has been discovered to interconnect the vastly fragmented space of robotic comprehension algorithms through the mechanism of , a generalization of the well-known scene graph. Therefore, we finally shape our vision for the future of robotic situational awareness by discussing interesting recent research directions.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23104849