Robot loopback detection method and device

Abstract

The invention provides a robot loopback detection method and device, and the method comprises the steps: obtaining a current image collected by a robot, inputting the current image to a densely connected convolutional neural network DenseNet, and obtaining a global feature; Wherein the densely connected convolutional neural network DenseNet is composed of multiple layers of dense blocks, and eachlayer of dense blocks is connected with other layers of dense blocks in a feedforward mode; According to a feature mapping decoupling algorithm, decoupling the global feature to obtain a local feature; Encoding the local features according to a weighted local feature aggregation descriptor encoding algorithm to obtain an encoding result; And calculating a first local sensitive hash value corresponding to the coding result, and determining a target image similar to the current image according to the first local sensitive hash value. According to the method, the robustness of resisting transformation of view angles, illumination, seasons and the like when the robot performs loop detection can be improved, and meanwhile, the recognition capability of different scenes including similar textures or similar surface features is improved.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to a method and device for loopback detection of a robot. Background technique [0002] With the continuous development of artificial intelligence technology, robots are becoming more and more popular. At present, for mobile robots, visual location recognition is an important part of relocation or loop detection. If the robot can distinguish that the current scene is the same as the scene it has seen before, then the robot can use this information to Repositioning is performed to correct historical errors accumulated by the previous Simultaneous Localization and Mapping (SLAM) system. [0003] However, due to factors such as light, seasons, and viewing angles, the same scene may have different characteristics at different times, and two different scenes may also contain similar textures or similar surface features, resulting in The case of robot misidentification....

AI Technical Summary

Problems solved by technology

In addition, it can also improve the robustness of the robot against changes in viewing angle, illumination, season, etc. when performing loopback detection. At the same time, it improves the ability to recognize different scenes containing similar textures or similar surface features, which is used to solve existing technologies. The bag-of-words model in it is difficult to identify scenes with changing ambient light and scenes with similar texturesTechnical issues

Images