In resent year, the demand for the autonomous mobile robots which can navigate indoor environment, such as office room, warehouse, hospital has increased. Autonomous mobile robot needs some information to navigate various environment. Self position is one of the important information since the robots need to determine its any behaviors such as path planning. Unfortunately this information is including some critical error such as odometry error which is caused by wheel slipping and uncertainty of its model parameters. Scan match is one of the method to overcome this problem. By determining two point cloud data measured by sensor (such as LIDAR) mounted on robot at different measurement point, it can estimate such errors. And furthermore, scan match is used to improve the proposal distribution of Rao-Blackwelized Particle filter using grid map. However, the computational cost of scan match is higher because searching nearest neighbor need a large amount of computational resources. To overcome this problem, we propose high efficient scan match method, using likelihood field map and hill-climb approach. And then we also propose efficient likelihood mapping method concurrently. In our experiments, efficiency of our scan match method was higher than two conventional method, ICP and correlative matching. And it verified that our likelihood field mapping method efficiency has been improved.