The work presents an efficient algorithm for object detection, orientation estimation, and isometric positioning in 3D space using RGBD camera data. The goal of the study is to improve the accuracy and processing speed of autonomous navigation and manipulation systems under conditions of limited computational resources. The proposed approach combines heuristic isometry estimation with segmentation methods (DBSCAN), plane estimation (RANSAC), and orientation analysis, enabling effective processing of scenes with planar backgrounds. The main advantage of the algorithm lies in its ability to operate in real time: the processing time for a single frame is only 20 ms, achieving object positioning accuracy up to 5.48 cm. The results of experimental research confirm a high level of accuracy and stability even under challenging conditions. The algorithm outperforms existing models in terms of processing speed while demonstrating comparable or superior positioning accuracy. The practical significance of the proposed method lies in its potential application in mobile robotics, automated warehouse systems, and machine vision systems where high autonomy and precision are required. The algorithm can also be adapted to a broader range of tasks due to its flexible hyperparameter tuning. A key limitation remains the requirement for object placement on a planar surface and the use of a depth camera, which necessitates a specific environmental setup. The proposed method makes a significant contribution to the advancement of computer vision and autonomous robotics technologies, opening prospects for its implementation in next-generation systems.