In this era of increasing technological advancement, where mankind is pushing the frontiers of exploration, systems are required to be increasingly autonomous and intelligent in order to work efficiently, and to provide optimum results. Unmanned vehicles generally use GPS to find their position in an environment and navigate. In this paper, we have studied and analysed two different algorithms used for navigating in a GPS denied environment, which is a more common scenario than navigating in a GPS-enabled environment. The SLAM algorithm generates a map of its environment by estimation and mapping, while the LIDAR maps its environment using pulses of a short wavelength. Both of them can be used for widely varying applications, and current researches in these fields are crucial in the development of autonomous systems.

There are many methods proposed for Back-ground Subtraction algorithm in past years. Background subtraction algorithm is widely used for real time moving object detection in video surveillance system. In this paper we have studied and implemented different types of meth-ods used for segmentation in Background subtraction algo-rithm with static camera. This paper gives good under-standing about procedure to obtain foreground using exist-ing common methods of Background Subtraction, their complexity, utility and also provide basics which will useful to improve performance in the future . First, we have explained the basic steps and procedure used in vision based moving object detection. Then, we have debriefed the common methods of background subtraction like Sim-ple method, statistical methods like Mean and Median filter, Frame Differencing and W4 System method, Running Gaussian Average and Gaussian Mixture Model and last is Eigenbackground Model. After that we have implemented all the above techniques on MATLAB software and show some experimental results for the same and compare them in terms of speed and complexity criteria.