# Vision-Aided Localization and Mapping in Forested Environments Using Stereo Images
## 1 Introduction  
The widespread adoption of sustainable forestry practices in much of the developed world has given rise to increasingly complex forest management objectives catering to a diverse array of interests. Consequently, silvicultural prescriptions often take into account numerous non-monetary factors, including forest resiliency and adaptability, wildlife conservation, aesthetic preservation, hydrological values, and other ecological functions that are dependent on stand structure [1]. In general, the incorporation of non-timber values into management objectives has served as the primary driver for transitioning silvicultural practices from homogeneous, even-aged systems to heterogeneous, uneven-aged systems. This paradigm shift directly affects forest operations; implementing complex silvicultural prescriptions becomes more costly in terms of layout, harvesting, and administration [2]. This challenge has spurred the demand for advanced precision forestry tools that offer accurate, real-time machine positioning, as well as forest measurement and mapping capabilities. Such tools have the potential to eliminate certain layout tasks, such as individual tree and boundary marking, thereby reducing operational costs and enhancing the economic feasibility of alternative silvicultural systems.  
Real-time machine positioning and forest mapping are essential components of sustainable forest harvesting [3]. However, practical low-cost technologies for precise localization and large-scale mapping in forested environments during harvesting remain underdeveloped. Unlike the substantial advancements in automation introduced in agricultural systems [4], forest harvesting has not experienced groundbreaking technological progress in terms of automation and robotics. This lag can be attributed, in part, to the challenging environments in which forest machines operate, as well as potential cultural factors, resistance to change, or inadequate research and development efforts.  
In this paper, we introduce visual-processing algorithms that facilitate precise real-time machine positioning and forest mapping. We employ visual egomotion estimation from a stereo camera to maintain the position of a forestry machine during instances of degraded or failed reception from a global navigation satellite system (GNSS). Furthermore, we demonstrate how precise position maintenance can be utilized to generate tree maps when combined with a tree-detection algorithm. Below, we itemize the main contributions of this work:  
* A novel camera-based system that integrates GNSS, visual odometry, and a tree-detection system and egomotion estimation.
* A low-cost system using an off-the-shelf stereo camera and a consumer-grade GNSS receiver.
* An efficient pixel-selection method with predictable inter-frame runtime for direct image alignment.
* A global orientation parameter in the optimization framework for preliminary alignment between the GNSS and visual odometry pose track.
* A system that provides the tree position and dendrometric information to the user in real time.  
In the remainder of this introduction, we will discuss the crucial role of GNSS in forest operations, along with the associated challenges and limitations. Additionally, we will introduce the concepts of visual odometry (VO) and simultaneous localization and mapping (SLAM).