This paper presents a vision-based localization and mapping algorithm for mobile robots using scale-invariant visual landmarks. The algorithm leverages the Triclops stereo vision system to estimate robot ego-motion and build a 3D map by matching scale-invariant feature transform (SIFT) landmarks across frames. The invariance of SIFT features to image translation, scaling, and rotation makes them suitable for robust localization and map building in unmodified environments. The paper details the SIFT feature extraction, stereo matching, ego-motion estimation, and landmark tracking processes. Experimental results demonstrate the effectiveness of the algorithm in a 10x10 m^2 laboratory environment, where the robot successfully localizes and builds a consistent 3D map. Error analysis using Kalman filters is conducted to account for noise and occlusions, resulting in a database map with landmark positional uncertainty. The proposed approach is compared with existing methods, highlighting its advantages in terms of robustness and efficiency.This paper presents a vision-based localization and mapping algorithm for mobile robots using scale-invariant visual landmarks. The algorithm leverages the Triclops stereo vision system to estimate robot ego-motion and build a 3D map by matching scale-invariant feature transform (SIFT) landmarks across frames. The invariance of SIFT features to image translation, scaling, and rotation makes them suitable for robust localization and map building in unmodified environments. The paper details the SIFT feature extraction, stereo matching, ego-motion estimation, and landmark tracking processes. Experimental results demonstrate the effectiveness of the algorithm in a 10x10 m^2 laboratory environment, where the robot successfully localizes and builds a consistent 3D map. Error analysis using Kalman filters is conducted to account for noise and occlusions, resulting in a database map with landmark positional uncertainty. The proposed approach is compared with existing methods, highlighting its advantages in terms of robustness and efficiency.