SLAM(Simultaneous Localization and Mapping) for autonomous mobile robotics
To implement autonomous driving, which has recently drawn attention, location, decision-making, and path generation must be completed. Therefore, we would like to implement SLAM with other tasks such as object tracking, path planning, and path tracking through 2D and 3D LiDAR.
Accurate distance data is obtained by a LiDAR sensor. And we generate a precision map from point cloud data containing distance information. We perform autonomous driving by comparing the generated map and current data in real-time to determine the current location.
Autonomous driving vehicle and drone, Hazardous Area Exploration Robot
Autonomous landing of drone
Recently, UAVs(Unmanned Aerial Vehicles) are actively used for various purposes such as research, surveillance, agriculture, and military. The position of the landing site is improper for the UAV to land due to the sudden movement of the ship or the shaking of the ship. Landing on a ship as well as on a moving mobile platform has similar difficulties. For these reasons, accurate and precise landing control is required.
This study is to develop a technology capable of high-speed automatic take-off and landing based on a camera/lidar fusion sensor that utilizes the advantages of a camera with a wide measurement area and a 3D Lidar with high distance measurement accuracy in an UAV.
After recognizing a landmark using camera-based image recognition technology, the distance to the UAV can be measured quickly and precisely by using a 3D lidar.
It is possible to control faster than the existing image-based distance control through the rapid acquisition of distance information of the Lidar, so it is possible to take off and land on a moving platform having a high speed of 30km/hr or more and shaking, such as a ship, as shown in the image.