Ego-motion sensors are commonly used for pose estimation in Simultaneous Localization And Mapping (SLAM) algorithms. Inertial Measurement Units (IMUs) are popular sensors but suffer from integration drift over longer time scales. To remedy the drift they are often used in combination with additional sensors, such as a LiDAR. Pose estimation is used when scans, produced by these additional sensors, are being matched. The matching of scans can be computationally heavy as one scan can contain millions of data points. Methods exist to simplify the problem of finding the relative pose between sensor data, such as the Normal Distribution Transform SLAM algorithm. The algorithm separates the point cloud data into a voxelgrid and represent each voxel as a normal distribution, effectively decreasing the amount of data points. Registration is based on a function which converges to a minimum. Sub-optimal conditions can cause the function to converge at a local minimum. To remedy this problem this thesis explores the benefits of combining IMU sensor data to estimate the pose to be used in the NDT SLAM algorithm.

Autonomous vehicles in the recent era are robust vehicles that have the capability to drive themselves without human involvement using sensors and Simultaneous Localization and Mapping algorithms, which helps the vehicle gain an understanding of its environment while driving with the help of laser scanners (Velodyne), IMU and GPS to collect data and solidify the foundation for locating itself in an unknown environment. Various methods were studied and have been tested for increasing the efficiency of registration and optimization over the years but the implementation of the NDT library for mapping and localization have been found to be fast and more accurate as compared to conventional methods. The objective of this thesis is to ascertain a robust method of pose estimation of the vehicle by combining data from the laser sensor, with the data from the IMU and GPS receiver on the vehicle. The initial estimate prediction of the position is achieved by generating a 3D map using the Normal Distribution Transform and estimating the position using the NDT localization algorithm and the GPS data collected by driving the vehicle in an external environment. The results presented explain and verify the hypothesis being stated and shows the comparison of the localization algorithm implemented with the GPS receiver data available on the vehicle while driving. / Autonoma fordon har på senare tid utvecklats till robusta fordon som kan köra sig själva utan hjälp av en människa, detta har möjliggjorts genom användandet av sensorer och algoritmer som utför lokalisering och kartläggning samtidigt (SLAM). Dessa sensorer och algoritmer hjälper fordonet att förstå dess omgivning medan det kör och tillsammans med laser skanners (Velodyne), IMU'er och GPS läggs grunden för att kunna utföra lokalisering i en okänd miljö. Ett flertal metoder har studerats och testats för att förbättra effektiviteten av registrering och optimering under åren men implementationen av NDT biblioteket för kartläggning och lokalisering har visat sig att vara snabbt och mer exakt jämfört med konventionella metoder. Målet med detta examensarbete är att hitta en robust metod för uppskatta pose genom att kombinera data från laser sensorn, en uppskattning av den ursprungliga positionen som fås genom att generera en 3D karta med hjälp av normalfördelningstransformen och GPS data insamlad från körningar i en extern miljö. Resultaten som presenteras beskriver och verifierar den hypotes som läggs fram och visar jämförelsen av den implementerade lokaliseringsalgoritmen med GPS data tillgänglig på fordonet under körning.