Zhaoxin Wang
ABSTRACT
In order to solve the problem of terrain recognition in the case of sample data and improve the terrain detection ability of unmanned platform in complex environment, a visual touch fusion idea is put forward. This idea is based on the original broad learning, by establishing a multimodal cascade feature node broad learning framework for terrain detection. First, we extract the initial features of touch and vision, and fuse them to obtain a fusion feature matrix. Then, the broad learning classifier is used to process the fusion feature matrix and get the result of terrain recognition. Finally, we use a self-built visual-tactile terrain dataset for experimental verification. The experimental results show that CFBRL-KCCA algorithm proposed gets the highest classification among several algorithms. As a result, it reached 84.67 percent. This provides an effective strategy for terrain detection by unmanned platforms in complex environments.
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Index Terms
- Multimodal Fusion Method for Complex Terrain Detection of Unmanned Platform
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