landmarkbased

Landmarkbased refers to approaches and systems that rely on identifiable environmental features, or landmarks, to perform tasks such as localization, mapping, and navigation. In landmarkbased methods, the position or state of a vehicle, robot, or observer is inferred from observations of fixed, recognizable elements in the environment rather than solely from odometry or global coordinates.

In robotics and autonomous systems, landmarkbased techniques are common in simultaneous localization and mapping (SLAM) and in real-time localization. Landmarks can be natural features such as buildings, trees, or road signs, or artificial markers like QR codes or specially designed tags. The process typically involves detecting landmarks with sensors such as cameras or LiDAR, estimating the relative pose to each landmark, and fusing these observations to update the system’s pose and map. Data association, filtering (e.g., Kalman or particle filters), and optimization methods like graph-based SLAM or bundle adjustment are often employed to improve accuracy.

Common implementations include marker-based localization using predefined tags (AprilTags, ArUco markers) and feature-based localization using distinctive environmental features. Landmarkbased approaches are also used in augmented reality and geospatial data collection, where stable landmarks provide reference points for alignment and measurement.

Advantages of landmarkbased methods include robustness to drift, the ability to operate in environments with limited prior maps, and the potential for accurate scaling when landmarks have known properties. Limitations arise from dependence on landmark visibility, occlusion, dynamic environments, and the need for reliable landmark detection and data association under varying conditions.