In an era where industries are becoming increasingly automated, the role of robotic manipulators is vital, particularly in tasks such as various pick-and-place operations. To enhance the capabilities of robotic manipulators, this study develops a rapid detection and localization method for simple objects using a depth camera. The method is implemented on an Kawasaki Astorino 3D-printed low-cost robot connected to a low-performance personal computer running ROS2. The method-ology involves the development of a detection method that uses a combination of a Haar cascade classifier and a Support Vector Machine (SVM) with Histogram of Oriented Gradient (HOG). Utilizing the information from the depth camera, the system determines the pose of detected objects in the robot’s base frame. The models are subsequently trained, tested, and evaluated with two types of objects: boxes and cylinders. The results are promising. Both models achieve a precision score of 1 and a recall score of approximately 0.85. Remarkably, both operate at a speed exceeding 60 FPS. Furthermore, grasp tests yield a success rate of at least 80%. The finding of the research was conducted as part of the MASTERLY project and not only solves the need for fast and efficient object detection and localization in the context of friendly-budget systems but also paves the way for more advanced applications not only in manufacturing but also in other industries and adaptive pick-and-place tasks.