Thanks to advancements in sensor technology, it is now possible to create wireless sensor networks that can be used for a variety of purposes, including locating objects, industry, and healthcare. These applications must continue as long as people want them to. One of the most important types of location navigation services is indoor mapping. Simultaneous localization and mapping (SLAM) is a crucial technique in an indoor navigation system using a low-cost 2-D LIDAR (Light Detection and Ranging) system from Slamtech. SLAM seeks to simultaneously produce a uniform, global map of the environment. In this paper, we propose the design of a system that executes two models concurrently and in real-time, which are considered crucial in recent years, especially in computer vision and artificial intelligence. The first one is to build a floor mapping based on simultaneous localization and mapping (SLAM). SLAM is often used to map indoor areas and localize handheld systems. The second one is for object detection based on the COCO dataset and using a convolutional neural network (CNN). These two models are implemented in the Raspberry Pi 4, which has less memory and processing capacity but is capable of handling massive quantities of data. Our system made the working of the RPLIDAR A1 smart because, in addition to its main work of mapping, it also added object detection.