Method of building map of mobile platform in dynamic environment
AssigneeSamsung Electronics Co., Ltd.
InventorsSeung Yong Hyung, Kyung Shik Roh, Suk June Yoon, Sung Hwan Ahn
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Abstract
Disclosed herein is a method of building a map of a mobile platform moving in a dynamic environment and detecting an object using a 3D camera sensor, e.g., an IR TOF camera sensor, for localization. A localization technology to separate and map a dynamic object and a static object is applied to a mobile platform, such as an unmanned vehicle or a mobile robot. Consequently, the present method is capable of accurately building map information based on the static object in a dynamic environment having a large number of dynamic objects and achieving a dynamic object avoidance or chasing function using position information acquired to build the map.
Claims23
A method of building a map of a mobile platform, comprising:
- acquiring a three-dimensional (3D) image of an environment in which the mobile platform moves;
- extracting feature points from the 3D image to estimate motion states of the respective feature points;
- clustering the feature points based on the motion states of the respective feature points to divide the feature points into a dynamic map and a static map;
- and
- building the mobile platform map based on the static map.
A method of building a map for a movable unit in a dynamic environment, comprising:
- acquiring a three-dimensional (3D) image of an environment having a dynamic object;
- extracting feature points from the 3D image to estimate motion states of the respective feature points;
- clustering the feature points based on the motion states of the respective feature points to divide the feature points into a dynamic map and a static map;
- and
- building the movable unit map based on the static map.
A method of creating a dynamic object avoidance path of a mobile platform, comprising:
- acquiring a three-dimensional (3D) image of an environment in which the mobile platform moves;
- extracting feature points from the 3D image to estimate motion states of the respective feature points;
- clustering the feature points based on the motion states of the respective feature points to divide the feature points into a dynamic map and a static map;
- and
- creating the dynamic object avoidance path based only on the dynamic map.
Dependencies are inferred from the claim text.