7. Multimodal visual positioning applications

Note: The Raspberry Pi 5 1GB without a large model package cannot run. Due to performance limitations, the 2GB and 4GB versions cannot run offline. You can refer to the tutorials for online large models.

1. Concept Introduction

1.1 What is "Multimodal Visual Localization"?

Multimodal visual localization is a technology that combines multiple sensor inputs (such as cameras, depth sensors, and IMUs) with algorithmic processing techniques to accurately identify and track the position and posture of a device or user in an environment. This technology does not rely solely on a single type of sensor data, but instead integrates information from different perception modalities, thereby improving localization accuracy and robustness.

1.2 Brief Implementation Principle

1. Cross-modal Representation Learning: In order for LLM to be capable of processing visual information, a mechanism must be developed to transform visual signals into a form that the model can understand. This may involve extracting features using convolutional neural networks (CNNs) or other architectures suitable for image processing and mapping them into the same embedding space as text.

2. Joint Training: By designing appropriate loss functions, text and visual data can be trained simultaneously within the same framework, allowing the model to learn to connect the two modalities. For example, in a question-answering system, an answer can be given based on both a textual question and the associated image content.

3. Visually Guided Language Generation/Understanding: Once effective cross-modal representations are established, visual information can be leveraged to enhance the capabilities of the language model. For example, given a photo, the model can not only describe what is happening in the image, but also answer specific questions about the scene and even execute commands based on visual cues (such as navigating to a location).

2. Code Explanation

Key Code

1. Tools Layer Entry (largemodel/utils/tools_manager.py)

The visual_positioning function in this file defines the tool's execution flow, specifically how it constructs a prompt containing the target object name and formatting requirements.

# From largemodel/utils/tools_manager.py
class ToolsManager:
    # ...
    def visual_positioning(self, args):
        """
        Locate object coordinates in image and save results to MD file.
        
        :param args: Arguments containing image path and object name.
        :return: Dictionary with file path and coordinate data.
        """
        self.node.get_logger().info(f"Executing visual_positioning() tool with args: {args}")
        try:
            image_path = args.get("image_path")
            object_name = args.get("object_name")
            # ... (Path fallback mechanism and parameter checking)
            
            # Construct a prompt asking the large model to identify the coordinates of the specified object. 
            if self.node.language == 'zh':
                prompt = f"请仔细分析这张图片，用一个个框定位图像每一个{object_name}的位置..."
            else:
                prompt = f"Please carefully analyze this image and find the position of all {
 Multimodal Video Analysis Application
Multimodal table scanning application