VSLAM method based on multi-characteristic visual odometer and graph optimization model

Abstract

The invention discloses a VSLAM method based on a multi-characteristic visual odometer and a graph optimization model, and belongs to the robot SLAM field; the method comprises the following steps: firstly using a FAST (features from accelerated segment test) and an improved LSD algorithm to extract point and line features in a color image; further using different descriptors to describe characteristics; then carrying out characteristic matching; finally using an improved NICP (normal iterative closest point) algorithm and a PnL (perspective n line) algorithm to estimate a robot initial posture. The method can extract image line features so as to enlarge algorithm application scenes, can obtain a well robot initial posture, uses a Bayes network to express a multi-characteristic visual odometer, obtains a factor graph on the basis of the Bayes network, uses a maximum posterior probability to estimate a robot global posture in the factor graph, and uses a Gauss-Newton method to solve themaximum posterior probability to obtain the updated posture graph; finally, the posture graph and three dimensional points of corresponding frames are fused to obtain a reconstructed three dimensional map.

Description

technical field [0001] The invention belongs to the field of robot SLAM (Simultaneous Localization and Mapping). Combined with the improved LSD (Line Segment Detector) algorithm, the extraction of the feature line segments in the collected images is completed, especially a method for creating a 3D map based on a graph optimization model. Background technique [0002] In recent years, with the rapid development of computer vision equipment and methods, the visual SLAM method has been widely used in robot human-computer interaction, unmanned driving, unmanned aerial vehicles, virtual reality and augmented reality, etc. Many fields. VSLAM uses images as the source of environment perception information to estimate the camera pose, and reconstructs the 3D map through multi-view geometry theory. [0003] The visual odometry (Visual Odometry) algorithm is an important pose estimation method, which only relies on visual information to obtain the absolute pose of the robot (camera)...

AI Technical Summary

Problems solved by technology

However, when the robot motion is large, the reliability of the robot pose calculated by the direct method based on gradient search is low, so the direct method is only suitable for occasions where the robot motion is small

In addition, the VSLAM algorithm based on visual odometry undergoes long-term frame-to-frame registration, and the resulting robot pose will have cumulative errors.

Traditional methods for eliminating cumulative errors are usually based on probabilistic frameworks (such as Kalman filtering), but they are computationally intensive and are mainly used in small and medium-sized scenarios

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