Camera mounted on the robot continuously take photos of the environment. From these photos we can extract recognizable and unique features. Base on the number of features in image frames and the similarity between frames, we can pick out the most representative data, namely the key frames, from large amounts of image information, and transmit these key frames, vehicle posture information and matching information between key frames to the cloud server via internet.
After the cloud server obtained key frame information, vehicle posture information and matching information between the key frames，with diagram optimization method it establish the equation to all the databased on the observation model and the kinematic model, furthermore,it uses iterative method to minimize the error to build an off-line 3D map with visual features,
In the process of navigation, robots continuously transmit key frame information to the cloud server. The cloud server then base on the matched degree among key frames, feature appearing frequency and the weight of dynamic adjustment features, discard the features with low observation frequency and continuously optimizes the 3D position with high observation frequency features.
For forklift type robot scheduling is very difficult. Different from other vehicle-scheduling problem, forklift robot scheduling problems are as follows:
① Travel distance is relatively short, which is different from car scheduling because car can be regarded as a point on the road.
② Collision, livelock, and deadlock issue happen easily due to the narrow space and low road capacity.
That is the reason why the forklift robot cannot use normal car scheduling method. Instead, the forklift robot must use customized scheduling methods with a large number of calculations. Thus, the cloud server is indispensable. We use the time window method and area control method to schedule the forklift robot from the cloud.
2.Easy to use.