This project is aimed at producing robust and efficient SLAM algorithm for indoor and outdoor navigation of mobile robots . SLAM implementations reported in literature vary depending upon the use of sensors for localization and mapping and the use of filtering algorithms . Proprioceptive sensors (such as encoders, gyroscope and accelerometer) have generally been used for localization . An increasing trend is to use exteroceptive sensors (such as laser range finders, and depth cameras) for localization thanks to their convenience of use and efficiency .

       Mapping the environment requires sensors that can observe features of the environment . The most popular exteroceptive sensors used for mapping include laser range finders, depth cameras, and RGB cameras . Apart from the use of sensors, the SLAM implementations differ in their use of filtering algorithm they employ . The most common filtering algorithms applied are particle filter, extended Kalman filter, information filter, and Rao – Blackwell algorithm .

       Our first implementation of SLAM will utilize both laser range finders and RGB - D sensors . Both sensors are used to find distance to the near obstacles and have been independently used in many SLAM implementations . Laser range finders (for example SICK LMS 500 laser scanner) provide distance and angle data output in a 2 – dimensional field . Same information can be extracted from RGB - D cameras (such as Kinect) mounted in specified directions . The accuracy of the sensor measurement can be greatly improved if data from both the sensors is utilized using an efficient sensor fusion algorithm . We plan to use unscented Kalman filter (UKF) which is widely reported to be more accurate as compared to conventionally used extended Kalman filter .

       The project aims to produce at least three variants of SLAM using various sensors and filtering algorithms . A variant of the above implementation would use Rao – Blackwellised particle filter with laser range finders and RGB - D sensor . The accuracy of localization can be greatly improved if data from inertial sensors and encoders is used together with laser range finder . Our third variant of SLAM implementation will use data from encoders and inertial sensors along with laser range finders to implement SLAM algorithm . The results of each implementation will be shared with robotics community through publication in reputed journals/conferences .

       This project is a continuation of our ongoing NRPU project where we are developing SLAM algorithms for indoor and outdoor mobile robots. A logical next step is to use those algorithms along with path planning techniques to develop a completely autonomous robotic vehicle. The project aims at developing a completely autonomous vehicle that has the capability to navigate in a completely unknown outdoor environment. By autonomous navigation, we mean the ability to autonomously go from an initial position to a final position in a completely unknown environment. The unknown environment means that the position of obstacles between the initial position and destination position are unknown; the nature of the terrain is also unknown; and additionally the environment is dynamic in nature meaning there could be moving obstacles in the path.