You have Bayesian range-only SLAM, iterative closest point (ICP) SLAM, particle filters like Rao-Blackwellised particle filtering (RBPF) SLAM, visual SLAM based on maximum likelihood estimation like Bundle-Adjustment, extended Kalman filter, so on and so forth; mix and match to get the right results.
SLAM being a popular research area, published algorithms find implementations in self-driving cars, drones, autonomous underwater vehicles, planetary rovers, domestic robots and even inside the human body.
Make your own robot
Common robotic platforms and sensors, Monte Carlo localisation, reactive navigation and such details have their own off-the-shelf modules that come as part of this open source suite, forming the perfect base for beginning a new robotic application from scratch.The toolkit also comes with Robot operating system (ROS) wrapper packages, coming as a boon to designers working on ROS. Before you begin, have a look at the list of sensors and platforms supported by the toolkit, on their website. The only thing that now limits your robot becoming a super robot is the code.
Priya Ravindran is M.Sc (electronics) from VIT University, Vellore, Tamil Nadu. She loves to explore new avenues and is passionate about writing