The traditional visual slam technology has the following characteristics and problems

1. Mapping based on feature points:

Traditional visual slam mainly relies on extracting feature points from images for scene localization and map construction.

2. Sparse feature information in solar scenarios:

In solar scenes, high image similarity results in relatively sparse feature point information extracted, which limits the performance of slam systems.

3. External data failure of the camera:

Due to the possible lack of texture or distinct features on the surface of solar panels, the way cameras capture external data may fail in this scenario.

4. Poor positioning effect:

Due to the sparsity of feature information and the problem of cameras capturing external data, traditional visual slam is difficult to achieve good localization performance in solar scenes.

5. Difficulty in ensuring safety and cleaning quality:

Due to poor positioning performance, traditional visual slam may not be able to ensure the safety and cleaning quality of robots in solar scenarios.

Kwunphi semantic visual technology

1. Actual scenario training:

Kwunphi train through a large amount of actual scene data, enabling computers to deeply understand image or video content. This effectively solves the problem of poor localization performance caused by sparse feature information in traditional slam in solar scenarios.

2. Self developed advanced semantic vision technology:

Guai bu has developed advanced semantic vision technology, which not only leverages the advantages of slam, but also extracts richer semantic information through deep learning. This enables machines to have a more comprehensive understanding of scenes, objects, and behaviors, improving reliability in complex environments.

3. High precision recognition and multimodal fusion:

The use of deep learning and advanced algorithms by strange insects has improved the accuracy and precision of pollutant identification, scene understanding, and behavior analysis. By combining visual information with other sensor data through multimodal fusion, the overall understanding ability of complex scenes has been improved.

4. Autonomous decision-making and path planning:

Kwunphi has the function of autonomous decision-making and emergency response, and makes decisions and plans cleaning paths independently by comprehensively considering various sensor information. This helps to adapt to environmental changes and ensures that robots can maintain a safe operating environment in any situation.

5. Real time feedback and adjustment:

The visual system of the Kwunphi supports real-time feedback and adjustment. The robot can update its positioning and behavior in real time to adapt to possible changes in solar scenes. This ensures the safety and cleaning quality of robots during cleaning operations.

Through these innovative technological applications, Kwunphi has successfully overcome a series of problems of traditional slam technology in solar scenarios, improving performance and efficiency in practical cleaning operations.

Kwunphi's visual positioning technology enables robot to move straight and accurately