Review of SLAM Algorithms for Indoor Mobile Robot with LIDAR and RGB-D Camera Technology

Abstract

Simultaneous localization and mapping are techniques of mapping the homogeneous environments by localizing the sensors’ position in an environment with centimetre accuracy. This paper reviews the different techniques used in mapping and localization of mobile robot and designing of low-cost mobile platform with sensors like RPLIDAR and Microsoft Kinect. This paper also discussed the use of Robot Operating System and different packages and topics used for implementation of SLAM. Along with software implementation of ROS, this paper also covers the comparison of different hardware boards such as NVIDIA Jetson Nano and NVIDIA TK1 GPUs for running heavy and complex CUDA algorithms. Lower slave boards such as Arduino Mega and STM32 are also discussed in this paper. This paper also focuses on use of different localization techniques such as AMCL, ORB-SLAM, Hector SLAM, Gmapping, RTAB-MAP and particle filter SLAM. Robot Operating System played the crucial role in all the complex processing and communication between different running nodes. Autonomous navigation is achieved using ROS navigation stack, and simulations for the same are done in Rviz and gazebo real-time environments. 2D point cloud-based algorithms with laser scanner are compared against the 3D visualization techniques. At the end, clear comparison between all the benchmark algorithms such as Hector SLAM, Gmapping, RTAB-MAP, ORB-SLAM and ZEDfu is done for clear understanding while selecting an algorithm for future research.