Vision-guided robotic manipulation plays a central role in flexible and low-cost automation systems, particularly for structured pick-and-place tasks. This paper presents a proof-of-concept study focused on vision-based perception and coordinate mapping for a delta robot, using a chessboard as a structured benchmark environment. The chessboard provides a regular grid with known geometry, enabling a systematic evaluation of computer vision techniques for object localization and robot-oriented spatial mapping. The proposed framework prioritizes visual perception over advanced control, aiming to extract reliable spatial information from camera input and convert it into robot-ready coordinates. A camera-based vision pipeline is developed using OpenCV to detect the chessboard, estimate its pose through corner detection and homography, and segment individual squares of the board. Chess piece presence and position are determined through color segmentation and contour analysis, allowing square occupancy estimation and centroid extraction. Camera calibration and board-plane registration enable the transformation of image coordinates into the delta robot workspace, providing target positions for pick-and-place actions. To assess robustness and performance, classical computer vision approaches are compared with convolutional neural network-based classifiers integrated via OpenCV’s DNN module for chess piece detection and classification. The methods are experimentally evaluated under varying lighting conditions in terms of detection accuracy, processing latency, and computational load. The results highlight practical trade-offs between classical and learning-based vision techniques for structured manipulation tasks, particularly regarding robustness and real-time feasibility. Although demonstrated in a chessboard scenario, the proposed approach is directly applicable to grid-based industrial operations such as kitting, tray loading, and fixture-based assembly. This work establishes a practical foundation for vision-based coordinate mapping in delta robot pick-and-place applications and supports future extensions toward more complex perception and manipulation strategies.