About self-positioning estimation and SLAM with LiDAR

In order for a robot to guide people to a destination or carry objects, it needs to know exactly where it is on the area map with respect to the destination.

SLAM (Simultaneous Localization and Mapping) is one of the basic technologies for that purpose, and SLAM has become an indispensable technology for autonomous driving support, which has been actively developed recently. Hokuyo Electrics LiDAR is often used to realize SLAM.

SLAM can estimate the current self-position by comparing the current peripheral shape data obtained from LiDAR with the shape of the area map. In addition, if the area map is not given in advance, the robot itself must generate the area map.

The area map is generated by adding the geometric shape data obtained from LiDAR. The self-position is estimated using the area map generated so far, and new shape data is added based on that to expand the area of the area map. The technology that performs this self-position estimation and area map generation at the same time is called SLAM.

Many methods based on scan matching and Bayesian filters have been proposed as typical SLAM methods. In particular, scan matching ICP (Iterative Closest Point) and NDT (Normal Distributions Transform) are famous and are often used in practical autonomous mobile robots.

Recently, various SLAM methods have been released as ROS packages, so if you have LiDAR and a PC, it seems that you can get started relatively easily.

The Hokuyo LiDAR programming information site also has information on how to use ROS, so please refer to it as well.

In the challenge and Nakanoshima Challenge, the technology improvement of the SLAM technology itself is also actively used in the Challenge and Nakanoshima Challenge, and many LiDars of the North Yoen is also used to perform autonomous driving experiments.

With the expansion of service robots and autonomous driving, SLAM technology has evolved. Hokuyo Electric has contributed to the development of robots by providing a wide variety of LiDAR, which is indispensable for that purpose, for many years. Based on the know-how cultivated so far, we can propose the most suitable LiDAR and how to use it at the site of use.

In the future, the range of use of robots will continue to expand, such as delivery and dangerous work environments. Hokuyo Electrics LiDAR is also developing to meet the changing needs.