Forestry operations are inherently labor-intensive, hazardous, and time-consuming, creating a pressing need for robotic solutions. However, deploying robots in forest environments presents unique challenges due to dense vegetation, dynamic and uneven terrain, and limited Global Navigation Satellite System (GNSS) signal availability. These factors severely hinder reliable localization and navigation, which are critical for autonomous operation. This chapter examines the current state of the art in localization and Simultaneous Localization and Mapping (SLAM) methods tailored for forest robotics. We first provide an overview of foundational concepts in robotic localization and SLAM, followed by an analysis of existing approaches implemented in forestry settings. Key challenges such as environmental complexity, sensor limitations in GNSS-denied areas, and computational demands are critically evaluated. By synthesizing insights from recent research, this review aims to guide future advancements in autonomous forestry robotics, emphasizing the need for adaptive algorithms and multimodal sensor fusion to overcome the demanding conditions of forest ecosystems.