SLAM method based on semantic bundle adjustment method

A beam adjustment method and semantic technology, applied in image data processing, instruments, calculations, etc., can solve problems such as no effective use, construction of known maps, combined evaluation of objects and cameras, etc., to achieve strong practicability and simple method , easy to achieve effect

Inactive Publication Date: 2016-10-05
BEIJING ROBOTLEO INTELLIGENT TECH
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Problems solved by technology

In some schemes, although the detection scheme is closely combined with the SLAM framework, it does not attempt to estimate the actual position of the target object; the rest uses geometric information for continuous target detection, but does not effectively use the original detected objects to construct known maps; there are also algorithms that use standard feature-based pipelines to detect objects and estimate relative poses for positional representations that use laser and odometry data to detect objects on maps, but these algorithms still do not The position of the camera is combined for evaluation; altho

Method used

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  • SLAM method based on semantic bundle adjustment method
  • SLAM method based on semantic bundle adjustment method
  • SLAM method based on semantic bundle adjustment method

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[0029] The present invention will be further described below in conjunction with the accompanying drawings.

[0030] The present invention is achieved in the following manner: a SLAM method based on semantic beam adjustment, comprising the following steps:

[0031] Step 1. Determine a series of features of the detection target, and build a model database for each detection target: if the entire 3D model is available, the 3D key point detector and descriptor can be used; otherwise, the model needs to use a bunch of Normalize the image and provide 2D keypoint detectors and descriptors for the required features; the feature descriptors are saved for future matches; then, for feature locations, 3D coordinates are saved for the former, and 2D image coordinates and their associated perspective pose.

[0032] Step 2. As a new frame is available, extract the descriptive features for the new frame and match it with the model database, and then create a deterministic map for each given...

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Abstract

The invention provides an SLAM method based on a semantic bundle adjustment method, and belongs to the field of mobile robot simultaneous localization and mapping (SLAM). The method is characterized in that the method combines a 6DOF object and the camera attitude through new semantic global optimization, and can work under a 2D or 3D sensor. As semantic information is added, a target detection channel can be seamlessly integrated into a BA type optimization system of an SLAM system based on BA without peripherals. The method of the invention is simple and easy to implement, and has strong practicability. SLAM constraints can be used in robust target detection, and can adapt to a more complicated environment.

Description

technical field [0001] The invention relates to a SLAM (simultaneous localization and mapping) method based on a semantic beam adjustment method, belonging to the field of simultaneous localization and map creation (SLAM) of mobile robots. Background technique [0002] The visual SLAM (Simultaneous Localization and Mapping) problem involves the ability to gradually reconstruct maps and simultaneously localize sensing devices based only on visual cues. In the past decade, there has been remarkable progress in this field with the application of more efficient tools such as AR (Augmented Reality) and machine navigation and composition. [0003] Traditional solutions to SLAM problems are based on filtering techniques such as Kalman filters, in which visual features are tracked in terms of frames and their estimated 3D positions and uncertain camera positions. Since only a small fraction of the image pixels are tracked, this method can only create sparse maps. As an alternative...

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Application Information

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IPC IPC(8): G06T7/00
Inventor 廖鸿宇孙放
Owner BEIJING ROBOTLEO INTELLIGENT TECH
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