For robots used for the indoor environment detection, positioning and navigation with a Light Detection and Ranging system (Lidar), the accuracy of map building, positioning and navigation, is largely restricted by the motion accuracy. Due to manufacture error and transmission error of the mechanical structure, sensors easily affected by the environment and other factors, robots’ cumulative motion error is inevitable. This paper presents a series of methods and solutions to overcome those problems, such as point set partition and feature extraction methods for processing Lidar scan points, feature matching method to correct the motion process, with less computation, more reasonable and rigorous threshold, wider scope of application, higher efficiency and accuracy. While extracting environment features and building indoor maps, these methods analyze the motion error of the robot and correct it, improving the accuracy of movement and map without any additional hardware. Experiments prove that the rotation error and translation error of the robot platform used in experiments can by reduced by 50% and by 70% respectively. The methods evidently improve the motion accuracy with a strong effectiveness and practicality.
Pixel equivalent is an important parameter to describe the relationship between pixels of digital images and actual size of measured piece in a 2D image measuring system. It is mainly calibrated with the standard component method, which is traditionally off-line and requires measuring conditions and attitude of devices to remain constant while measuring and calibrating. To overcome above limitations, a new calibration method is proposed in this paper which is defined as the float equivalent method. This method requires the standard component and measured piece be placed in image measuring system simultaneously. Everytime before measuring, no matter aiming at the same measuring point or not, the pixel equivalent is calibrated for this specific time, specific condition, specific measuring point, and specific object distance. This method has the advantage of reducing the influence of conditions changing on the accuracy without additional calibration equipment or operations. The steel tape verification system is taken as an example to testify the effectiveness of the method.
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