In mobile robot localization and mapping, occlusion and falsified occupancy are major problems. The occlusion becomes a key issue when dealing with dynamic objects in cluttered environment in which multiple objects are placed randomly. The occluded dynamic objects may appear suddenly in the path of a mobile robot and can hinder it in performing the goal tasks. On the other hand due to falsified occupancy, objects in an image cover more ground plane than their actual 2-D base and reduce erroneously the available floor plane for navigation tasks. The reduction of free ground space places constraints on path planning algorithms. In this research, multiple cameras in the indoor environment are installed at such positions from where whole work space is in view of each camera. The approach is simplistic but results are very impressive. The synchronously captured images are stored on PC and processed for correction of projective distortions. The images are then registered with the front view of the environment. A novel binarized image fusion approach is used in which these aligned images are taken as input images and generate a single output image, representing the top-view of the environment. The objects in the fused output image are detected and their spatial positions are determined. The proposed image fusion method simultaneously solves the occlusion and ivfalsified occupancy problem. Static and dynamic objects of the scene are identified and localized. Occupancy grid map is developed with dynamic occupancy values and a parametric vector is calculated for each object. For validation of the proposed technique a prototype environment is used with various shapes of objects. Small scale experimental results show that occlusion and falsified occupancy problem can be resolved up to 99% for indoor applications.