In order to improve the autonomy of construction robots, a Simultaneous Localization and Mapping (SLAM) solution is needed that localizes the robot using solely on-board sensing with sub-5 mm accuracy. As pointed out by the results of the Hilti SLAM Challenge, the state-of-the-art in SLAM currently does not offer a solution that comes close to this requirement. The winning algorithm FAST-LIO2 achieved an accuracy typically around 4-10 cm. When considering the literature, it can be noted that the state-of-the-art SLAM algorithms are designed with generality in mind, and limited attention has been paid to SLAM that focuses on increasing accuracy by targeting a specific scene structure.

In order to improve SLAM accuracy, this thesis proposes a novel 2D Lidar SLAM algorithm focused on indoor environments, hereafter called Ray-SLAM. The algorithm is centered around the assumption that walls are available, which is valid in many cases on the building construction site. Ray-SLAM introduces several novelties which are (1) a sparse map representation to facilitate a joint pose-map optimization scheme, (2) observation-to-map alignment using a non-iterative procedure (contrary to the Iterative Closest Point algorithm) and (3) a stop-and-go strategy to prevent Lidar motion distortion to corrupt the map.

The algorithm was extensively tested using six real-world indoor datasets recorded with the Ouster OS0 Lidar in rooms with various shapes and sizes and with a total trajectory length of 307 m. The motion was constrained to the horizontal plane and a stop-and-go motion pattern was applied. Ground-truth was recorded at static points to evaluate the accuracy. In the majority of the test cases, Ray-SLAM was able to estimate the trajectory successfully. Failure cases led back to either a lack of unoccluded walls in the scene or violated model assumptions about these walls (e.g. incorrect referencing to clutter close to the wall or to a door being opened).

Ray-SLAM's accuracy was compared with two 3D Lidar SLAM algorithms, LOAM and FAST-LIO2 respectively. The compared algorithms solved a harder 6DOF estimation problem, but had access to 64 Lidar scan rings instead of one and (optionally) the IMU. An overall Mean Absolute Error of 5.7 mm is reported by Ray-SLAM in the successful cases, which is 5.0x more accurate than LOAM and 2.4x more accurate than FAST-LIO2. Further research is suggested to improve the robustness of Ray-SLAM and extend it to a full 3D SLAM algorithm.