Challenges and Solutions for Autonomous Ground Robot Scene Understanding and Navigation in Unstructured Outdoor Environments: A Review

Abstract

The capabilities of autonomous mobile robotic systems have been steadily improving due to recent advancements in computer science, engineering, and related disciplines such as cognitive science. In controlled environments, robots have achieved relatively high levels of autonomy. In more unstructured environments, however, the development of fully autonomous mobile robots remains challenging due to the complexity of understanding these environments. Many autonomous mobile robots use classical, learning-based or hybrid approaches for navigation. More recent learning-based methods may replace the complete navigation pipeline or selected stages of the classical approach. For effective deployment, autonomous robots must understand their external environments at a sophisticated level according to their intended applications. Therefore, in addition to robot perception, scene analysis and higher-level scene understanding (e.g., traversable/non-traversable, rough or smooth terrain, etc.) are required for autonomous robot navigation in unstructured outdoor environments. This paper provides a comprehensive review and critical analysis of these methods in the context of their applications to the problems of robot perception and scene understanding in unstructured environments and the related problems of localisation, environment mapping and path planning. State-of-the-art sensor fusion methods and multimodal scene understanding approaches are also discussed and evaluated within this context. The paper concludes with an in-depth discussion regarding the current state of the autonomous ground robot navigation challenge in unstructured outdoor environments and the most promising future research directions to overcome these challenges.

A Survey on Path Planning for Autonomous Ground Vehicles in Unstructured Environments

“…Bai et al [9] considered obstacle avoidance and TTA of path planning from the perspective of vision methods. Wijayathunga et al [25] summarized the path planning algorithms encompassing location and map creation methods.…”

“…Local path planning strategies generally utilize sensor data about their surroundings to respond to the changing dynamics of the environment [25]. Dynamic obstacle avoidance is crucial to ensure safety, which makes obstacle avoidance decisions based on the perception of the constantly changing local environment [123].…”

Emerging Technologies for Automation in Environmental Sensing: Review

“…Rovers, exemplified by notable systems like AIR-K and ROSI, showcase advancements in autonomous ground inspection and monitoring. These robots employ tracked wheels, sensors like cameras and gas sensors, and varying maturity levels to execute operations and observations in emergencies [32]. Additionally, minibots, with their tracked wheels and laser scanner sensors, offer versatility in ground exploration [33].…”

A Study on the UWB-Based Position Estimation Method Using Dead Reckoning Information for Active Driving in a Mapless Environment of Intelligent Wheelchairs

“…However, for current autonomous driving technology, prior data are essential to actively move to the destination based on the current position through the pre-map information [9]. Therefore, the current indoor autonomous driving technology cannot utilize autonomous driving in a mapless environment because it is difficult to create a route for driving to the destination through position estimation without prior map information [10].…”