Embedded system application development on raspberry Pi 3 : smart surveillance robot

Traditional SLAM (Simultaneous localization and mapping) robots made use of techniques such as the extended Kalman filter and fastSLAM. The extended Kalman filter is a recursive technique whereby estimation of the current state is based on measurement from the previous state. Firstly, the algorithm predicts the state of the robot based on some sensor values. Subsequently, the actual state of the robot is observed based on other sensor values. Actual state of robot may be wrong due to errors in sensor. Finally, the algorithm compares the two values and determines the best location for the state of the robot. The process is repeated for the subsequence state of the robot [1]. The above short explanation demonstrated the limitation of implementing such algorithms into low cost SLAM robots as the localization depends heavily on the precision of the sensor. A precise sensor is needed for accurate estimation. This limits the possibility of implementing SLAM into embedded systems found commonly in our daily life. The project look into the possibility of using the raspberry pi 3 embedded system board together with simple sensors to implement SLAM using a grid based technique. Unlike traditional techniques, the grid base technique is less sensitive to the precision of the sensors and will be able to better determine the position of the robot in places with fewer landmarks. Overall, the final product of the project is able to explore an indoor environment and produce a fairly accurate mapBachelor of Engineering (Electrical and Electronic Engineering