The concept of Automation began with the rise of Industrial Revolution 3.0. Automation was then done in a partial manner with help of computers and programmable controls. With Industrial Revolution 4.0, world started focusing on automation. Industries working on automating their factories and car manufacturers towards driverless cars. Nowadays, robots are being used for public services and transportation. For navigation purpose, these robots arestill using Global Positioning System (GPS), which is highly advanced but with its own uncertainties. To bypass the use of GPS in industry and implementation in autonomous cars, one can use combination of machine vision and photogrammetry techniques. The machine vision aspect is to detect multiple objects having known coordinates in wall coordinate system and identify the position of these objects by recognising their class value in an image. First step was to calibrate the camera being used for the work. It was done by using MATLAB’s inbuilt Camera Calibrator App, from where we derive the focal length of the camera. Next step was to detect and classify the objects present in the environment. After detection, the centroid values of each object in the image plane were determined. For the photogrammetry aspect, one should know these objects’ position in wall coordinate system, and then using the concept of collinearity and triangulation, combining it with the centroid values gained from machine vision aspect, one estimates the position of camera from which the video and images are being taken. The result of this part indicates a method where the camera can detect multiple objects in the image going from minimum of four to maximum of five with their centroid values and respective classes plotted on the image or the frames of the video. As the distance between the image and the projection centre is just the focal length of the camera, the method utilises the knowledge of basic trigonometry to determine angles between the projection centre of the camera and object coordinates in image and the angles are observed to be same for projection centre and actual objects in the environment. From the calculated distances between the objects in real time and the projection centre, one estimates the coordinate of the projection centre using a nonlinear least squares method. All these calculated values combined with the data gathered from the machine vision aspect, are used to estimate the camera position in an environment with some uncertainty. This uncertainty is the error in calculating the position of the camera. The result shows the proof of conceptual model and can be implemented by industries to overcome the issues faced with the use of GPS.