This paper presents a vision-based mobile robot localization and mapping (SLAM) algorithm using scale-invariant image features as natural landmarks in unmodified environments. The algorithm employs the Triclops stereo vision system to obtain 3D positions of landmarks, enabling simultaneous 3D map building and robot localization. SIFT features are used as landmarks due to their invariance to translation, scaling, and rotation, making them robust for SLAM. The algorithm tracks these features across frames using least-squares minimization to estimate robot ego-motion. A database of SIFT landmarks is maintained, with each landmark's position, scale, orientation, and view direction recorded. Error analysis is performed to account for landmark and robot position uncertainties, and Kalman filters are used to track landmarks in dynamic environments. The system is tested in a 10×10 m² laboratory environment, where thousands of SIFT landmarks are used to build a consistent 3D map. The algorithm is robust to viewpoint changes, occlusions, and dynamic environments, and it provides accurate robot pose estimates. The results show that the system can effectively localize the robot and build a 3D map using SIFT features, with high consistency and reliability. The approach is efficient and scalable, making it suitable for a wide range of mobile robot applications.This paper presents a vision-based mobile robot localization and mapping (SLAM) algorithm using scale-invariant image features as natural landmarks in unmodified environments. The algorithm employs the Triclops stereo vision system to obtain 3D positions of landmarks, enabling simultaneous 3D map building and robot localization. SIFT features are used as landmarks due to their invariance to translation, scaling, and rotation, making them robust for SLAM. The algorithm tracks these features across frames using least-squares minimization to estimate robot ego-motion. A database of SIFT landmarks is maintained, with each landmark's position, scale, orientation, and view direction recorded. Error analysis is performed to account for landmark and robot position uncertainties, and Kalman filters are used to track landmarks in dynamic environments. The system is tested in a 10×10 m² laboratory environment, where thousands of SIFT landmarks are used to build a consistent 3D map. The algorithm is robust to viewpoint changes, occlusions, and dynamic environments, and it provides accurate robot pose estimates. The results show that the system can effectively localize the robot and build a 3D map using SIFT features, with high consistency and reliability. The approach is efficient and scalable, making it suitable for a wide range of mobile robot applications.