# Monocular Vision-Based Depth Estimation of Forward-Looking Scenes for Mobile Platforms
## 1 Introduction  
An Intelligent Mobile Platform (IMP) is an advanced robotic system that operates autonomously without the need for direct human control. It is capable of autonomous sensing, decision-making, and task execution [1, 2]. Unmanned aerial vehicles (UAVs) and autonomous ground vehicles (AGVs) are the primary examples of IMPs [3, 4, 5]. With the rapid advancement of artificial intelligence, IMPs have found extensive applications across various domains. Their high level of automation and strong adaptability to diverse environments provide them with a significant advantage over manned systems, especially in challenging working conditions. IMPs can effectively replace humans in hazardous tasks, thereby significantly reducing safety risks. Environmental sensing is a fundamental task for ensuring the safe operation of IMPs [6]. Sensing technology equips IMPs with the capability to continuously detectpotential obstacles, other traffic participants, terrain changes, and other factors that may jeopardize operational safety. This technology enables IMPs to take timely measures to avoid collisions or other hazardous situations [7, 8]. A crucial component of environmental sensing is depth information, which refers to the distance of objects or points in the scene relative to the IMP's sensor. Depth information is essential for IMPs to understand the spatial relationships between objects in the environment [9]. Therefore, the primary objective of this paper is to obtain depth information from the forward-looking scene during the operation of an IMP, thereby mitigating the risk of conflicts and eliminating potential hazards to operational safety. The sensors used for depth measurement can be broadly categorized into two types: active and passive sensors. Active sensors, such as LIDAR and millimeter-wave radar, actively emit energy outward and rely on the reflection of this energy to directly obtain three-dimensional information about the scene [10, 11]. However, each laser point can only capture the depth of a single point on the object's surface, and as the measurement distance increases, the point cloud containing depth information becomes increasingly sparse. Additionally, the high cost of active sensors may hinder their widespread adoption in certain applications. Consequently, active sensors have limitations regarding their applicability to IMPs. In contrast, RGB cameras, which are representative of passive sensors, offer a low-cost and low-power solution while providing rich, detailed, and semantic information [12]. With advancements in computer vision technology and deep learning, it is now entirely feasible to extract depth information from images. Therefore, to address the challenge of obstacle perception for IMPs, this paper employs deep learning and neural network models to explore a method for estimating the depth of forward-looking scenes based on RGB sensors. Generally, vision-based depth estimation methods can be categorized into traditional methods and deep learning-based methods. The former relies on multiple-view geometry techniques, such as Structure from Motion (SfM) [13] and Visual Simultaneous Localization and Mapping (VSLAM) [14]. However, due to the high computational complexity associated with feature extraction and matching (including points, lines, and planes), traditional methods struggle to perform effectively in scenarios that demand high real-time performance. Furthermore, these approaches typically provide only sparse depth information, making it challenging to accurately estimate the dense depth of forward-looking scenes. The limitations of traditional depth estimation methods hinder their practical application in IMPs. In contrast, deep learning-based methods, particularly Convolutional Neural Networks (CNNs), demonstrate a remarkable capacity for feature representation, allowing for the extraction of depth information from images and the generation of pixel-level dense depth values [15]. Consequently, this paper focuses on a CNN-based approach for dense depth estimation.