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Review
. 2022 Nov 3;22(21):8463.
doi: 10.3390/s22218463.

Off-Road Detection Analysis for Autonomous Ground Vehicles: A Review

Fahmida Islam  1 , M M Nabi  1 , John E Ball  1
Affiliations
Review

Off-Road Detection Analysis for Autonomous Ground Vehicles: A Review

Fahmida Islam et al. Sensors (Basel). .

Abstract

When it comes to some essential abilities of autonomous ground vehicles (AGV), detection is one of them. In order to safely navigate through any known or unknown environment, AGV must be able to detect important elements on the path. Detection is applicable both on-road and off-road, but they are much different in each environment. The key elements of any environment that AGV must identify are the drivable pathway and whether there are any obstacles around it. Many works have been published focusing on different detection components in various ways. In this paper, a survey of the most recent advancements in AGV detection methods that are intended specifically for the off-road environment has been presented. For this, we divided the literature into three major groups: drivable ground and positive and negative obstacles. Each detection portion has been further divided into multiple categories based on the technology used, for example, single sensor-based, multiple sensor-based, and how the data has been analyzed. Furthermore, it has added critical findings in detection technology, challenges associated with detection and off-road environment, and possible future directions. Authors believe this work will help the reader in finding literature who are doing similar works.

Keywords: autonomous ground vehicles; drivable ground; negative obstacles; off-road environment; positive obstacles.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Examples of some off-road environments with unstructured roadways; The figure presents (a) rocky, (b) muddy, and (c) sandy road environments. Images are collected from the autonomous testing ground of the Center for Advanced Vehicular Systems (CAVS) at Mississippi State University (MSU).
Figure 2
Figure 2
Classification of detection for AGV navigation in the off-road environment.
Figure 3
Figure 3
Different types of obstacles found in the off-road environment.
Figure 4
Figure 4
Distribution of the literature in different categories.
See this image and copyright information in PMC

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