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.

翻译：同时定位与地图构建（SLAM）是自主机器人在未确定的无结构环境中构建或更新地图，并同时估计其自身位姿的问题。过去30年来，自动驾驶车辆的发展趋势推动了鲁棒SLAM技术的演进。该问题通过使用标准传感器或传感器阵列（超声波传感器、激光雷达、摄像头、Kinect RGB-D）结合传感器融合技术来实现感知步骤。传感方法根据环境特性确定以提取特征。随后讨论了经典的基于滤波器的方法、视觉SLAM中流行的全局优化方法以及基于卷积神经网络的方法（如深度学习SLAM），并探讨了如何解决定位与地图构建中的难题。本文对长期自主性中的鲁棒性与可扩展性、性能及其他算法新方向进行了比较与梳理。本文以评判性视角审视了已发表的先前工作，涵盖从传感器到算法的发展，同时讨论了开放挑战与新的研究前沿。