Physics

Archaeology

Mobile robot

Image (mathematics)

Optics

Stereo camera

Artificial intelligence

Geography

Focus (optics)

Obstacle

Computer science

Histogram

Computer vision

Monocular

Obstacle avoidance

Stereopsis

Robot

Remote Sensing

Mobile robots lack a driver or a pilot and, thus, should be able to detect obstacles autonomously. This paper reviews various image-based obstacle detection techniques employed by unmanned vehicles such as Unmanned Surface Vehicles (USVs), Unmanned Aerial Vehicles (UAVs), and Micro Aerial Vehicles (MAVs). More than 110 papers from 23 high-impact computer science journals, which were published over the past 20 years, were reviewed. The techniques were divided into monocular and stereo. The former uses a single camera, while the latter makes use of images taken by two synchronised cameras. Monocular obstacle detection methods are discussed in appearance-based, motion-based, depth-based, and expansion-based categories. Monocular obstacle detection approaches have simple, fast, and straightforward computations. Thus, they are more suited for robots like MAVs and compact UAVs, which usually are small and have limited processing power. On the other hand, stereo-based methods use pair(s) of synchronised cameras to generate a real-time 3D map from the surrounding objects to locate the obstacles. Stereo-based approaches have been classified into Inverse Perspective Mapping (IPM)-based and disparity histogram-based methods. Whether aerial or terrestrial, disparity histogram-based methods suffer from common problems: computational complexity, sensitivity to illumination changes, and the need for accurate camera calibration, especially when implemented on small robots. In addition, until recently, both monocular and stereo methods relied on conventional image processing techniques and, thus, did not meet the requirements of real-time applications. Therefore, deep learning networks have been the centre of focus in recent years to develop fast and reliable obstacle detection solutions. However, we observed that despite significant progress, deep learning techniques also face difficulties in complex and unknown environments where objects of varying types and shapes are present. The review suggests that detecting narrow and small, moving obstacles and fast obstacle detection are the most challenging problem to focus on in future studies.

Image-Based Obstacle Detection Methods for the Safe Navigation of Unmanned Vehicles: A Review