Visual SLAM Combined with Object Detection for Autonomous Indoor Navigation Using Kinect V2 and ROS

SLAM can be defined as exploring the unknown environment while mapping the robot's surroundings alongside estimating its pose (i.e., position and orientation). It is primarily done using the sensors mounted on the robot. SLAM enables us to autonomously navigate the robot throughout the map based on given final goal coordinates or waypoints. However, SLAM algorithms alone are not capable of performing complex tasks such as autonomous payload delivery in warehouses, healthcare facilities, etc. These tasks require additional semantic information about the environment. To solve this problem, we propose a solution where the traditional Visual SLAM method is accompanied by object detection using pre-trained CNNs to enhance the robot's capabilities of navigating efficiently and performing robust 3D perception in indoor environments. RTAB-Map using the KinectV2 RGB-D Camera is selected to perform Visual SLAM while the YOLO V3 tiny model acts as the CNN detector for detecting objects of interest. Development platform used is ROS & Gazebo. The proposed solution is experimentally verified by simulating the Turtlebot in the Gazebo environment.