  Simultaneous localisation and mapping (SLAM) is the problem of autonomous
robots to construct or update a map of an undetermined unstructured environment
while simultaneously estimate the pose in it. The current trend towards
self-driving vehicles has influenced the development of robust SLAM techniques
over the last 30 years. This problem is addressed by using a standard sensor or
a sensor array (Ultrasonic sensor, LIDAR, Camera, Kinect RGB-D) with sensor
fusion techniques to achieve the perception step. Sensing method is determined
by considering the specifications of the environment to extract the features.
Then the usage of classical Filter-based approaches, the global optimisation
approach which is a popular method for visual-based SLAM and convolutional
neural network-based methods such as deep learning-based SLAM are discussed
whereas considering how to overcome the localisation and mapping issues. The
robustness and scalability in long-term autonomy, performance and other new
directions in the algorithms compared with each other to sort out. This paper
is looking at the published previous work with a judgemental perspective from
sensors to algorithm development while discussing open challenges and new
research frontiers.
