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A Dense Map Creation Method Based on G2O and Random Fern Algorithm

A technology for random ferns and map creation, applied in computing, image analysis, mapping and navigation, etc., can solve problems such as accumulation and inability to build a globally consistent map

Active Publication Date: 2021-11-26
BEIJING UNIV OF TECH
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  • Description
  • Claims
  • Application Information

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Problems solved by technology

Since the original TSDF model only considers the correlation in adjacent time, the error will inevitably accumulate to the next moment, and it is impossible to build a globally consistent map

Method used

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  • A Dense Map Creation Method Based on G2O and Random Fern Algorithm
  • A Dense Map Creation Method Based on G2O and Random Fern Algorithm
  • A Dense Map Creation Method Based on G2O and Random Fern Algorithm

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Embodiment Construction

[0035] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0036] The invention is based on a Kinect sensor, collects data of the experimental environment through a map creation algorithm, and obtains corresponding depth maps and color maps. Optimize the depth map and calculate the rotation and translation matrix from the collected data information to create a camera pose model. The observation state with the smallest error is obtained by constructing the iterative function of g2o, using the GPU to process the ICP matching projection algorithm in parallel, constructing a model-to-model matching strategy, and using random ferns for closed-loop matching. Establish a TSDF model to represent the surface of a three-dimensional object and perform multi-frame fusion. The overall flow of the method involved is attached figure 1 As shown, the specific implementation process is divided into the following steps...

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Abstract

A dense map creation method based on g2o and random fern algorithm belongs to the field of robot real-time positioning and map creation. First, a camera pose model and a weighted TSDF model fused with 3D point truncated information are constructed to accurately represent the surface on which the object is created. Secondly, an improved loop closure detection method is proposed and combined with a random fern color image encoding strategy to optimize the TSDF model. Finally, use the g2o graph optimization library to solve the constraint function and establish the optimal edge between the data sets. Experimental results show that the hybrid optimized pose model can quickly establish a global SLAM map and effectively identify areas that have been reached.

Description

technical field [0001] The invention belongs to the field of robot real-time positioning and map creation. Use g2o to optimize the local and global loop detection tasks, use the random fern algorithm for closed loop matching, and build a TSDF (Truncated Signed Distance Function, TSDF) model. Background technique [0002] The present invention mainly solves the problem of robot V-SLAM (Vision-only Simultaneous Localization and Mapping, V-SLAM) using a visual sensor in an indoor environment. The main difficulty of SLAM is that precise map creation relies on an accurate estimate of the robot's position, which in turn comes from the sensor's perception of the outside world, that is, the landmarks in the map (Landmark). Therefore, "localization" and "mapping", as two coupled and interdependent problems, are susceptible to each other's noise. In addition, common phenomena in the SLAM process, such as Data Association, Loop Closure Detection, Global Optimization, etc., are also i...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/73G06T7/33G01C21/20
CPCG01C21/206G06T7/344G06T7/75
Inventor 贾松敏李柏杨张国梁李秀智张祥银
Owner BEIJING UNIV OF TECH
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