3. Multimodal visual positioning applications

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 Overview of Implementation Principles

1. Cross-modal Representation Learning: In order for LLMs 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 an appropriate loss function, text and visual data can be trained simultaneously within the same framework, allowing the model to learn to relate to these two modalities. For example, in a question-answering system, answers can be given based on both the provided text 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, when 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 instructions based on visual cues (such as navigating to a location).

2. Code Analysis

Key Code

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

The visual_positioning function in this file defines the execution flow of the tool, 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 {object_name}..."
            
            # ... (Build an independent message context)
            
            result = self.node.model_client.infer_with_image(image_path, prompt, message=message_to_use)
            
            # ... (Process and parse the returned coordinate text)
            
            return {
                "file_path": md_file_path,
                "coordinates_content": coordinates_content,
                "explanation_content": explanation_content
            }
        # ... (Error Handling)

2. Model interface layer (largemodel/utils/large_model_interface.py)

The infer_with_image function in this file is the unified entry point for all image-related tasks.

​x
# From largemodel/utils/large_model_interface.py
​
class model_interface:
    # ...
    def infer_with_image(self, image_path, text=None, message=None):
        """Unified image inference interface. """
        # ... (Prepare message)
        try:
            # Determine which specific implementation to call based on the value of self.llm_platform
            if self.llm_platform == 'ollama':
                response_content = self.ollama_infer(self.messages, image_path=image_path)
            elif self.llm_platform == 'tongyi':
                # ... Logic for calling the Tongyi model
                pass
            # ... (Logic for other platforms)
        # ...
        return {'response': response_content, 'messages': self.messages.copy()}

Code Analysis

The core of the visual positioning function lies in guiding large models to output structured data through precise instructions. It also follows the layered design of the tool layer and the model interface layer.

1. Tools Layer (tools_manager.py):

   • The visual_positioning function is the core of this function. It accepts two key parameters: image_path (the image path) and object_name (the name of the object to be positioned).
   • The core operation of this function is building a highly customized prompt. It doesn't simply ask the model to describe an image. Instead, it embeds object_name into a carefully designed template, explicitly instructing the model to "locate each {object_name} in the image," and implicitly or explicitly requires the results to be returned in a specific format (such as an array of coordinates).
   • After building the prompt, it calls the infer_with_image method of the model interface layer, passing the image and this customized instruction.
   • After receiving the returned text from the model interface layer, it needs to perform post-processing: using methods such as regular expressions to parse the model's natural language response to extract precise coordinate data.

• Finally, it returns the parsed structured coordinate data to the upper-layer application.

2. Model Interface Layer (large_model_interface.py):

   • The infer_with_image function still serves as the "dispatching center." It receives the image and prompt from visual_positioning and dispatches the task to the correct backend model implementation based on the current configuration (self.llm_platform).
   • For visual positioning tasks, the model interface layer's responsibilities are essentially the same as for visual understanding tasks: correctly packaging the image data and text instructions, sending them to the selected model platform, and then returning the returned text results intact to the tool layer. All platform-specific implementation details are encapsulated in this layer.

In summary, the general workflow for visual localization is: ToolsManager receives the target object name and constructs a precise prompt requesting coordinates. ToolsManager calls the model interface. model_interface packages the image and prompt together and sends them to the corresponding model platform according to the configuration. The model returns a text file containing the coordinates. model_interface returns the text file to ToolsManager. ToolsManager parses the text file, extracts the structured coordinate data, and returns it. This process demonstrates how prompt engineering techniques can be used to enable a general-purpose large visual model to accomplish more specific and structured tasks.

3. Practical Applications

3.1 Configuring Online LLM

First, enter the Docker container by entering the command in the terminal:

xxxxxxxxxx
./ros2_docker.sh

If you need to enter other commands in the same Docker container later, simply enter ./ros2_docker.sh again in the host terminal.

1. Next, you need to update the key in the configuration file. Open the model interface configuration file large_model_interface.yaml:

   xxxxxxxxxx
   vim ~/yahboom_ws/src/largemodel/config/large_model_interface.yaml
   

2. Enter your API Key: Find the corresponding section and paste the API Key you just copied. This example uses the Tongyi Qianwen configuration.

   xxxxxxxxxx
   # large_model_interface.yaml
   ​
   ## Thousand Questions on Tongyi
   qianwen_api_key: "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" # Paste your Key
   qianwen_model: "qwen-vl-max-latest" # You can choose the model as needed, such as qwen-turbo, qwen-plus
   

3. Open the main configuration file yahboom.yaml:

   xxxxxxxxxx
   vim ~/yahboom_ws/src/largemodel/config/yahboom.yaml
   

4. Select the online platform you want to use: Change the llm_platform parameter to the platform name you want to use.

   xxxxxxxxxx
   # yahboom.yaml
   ​
   model_service:
     ros__parameters:
       # ...
       llm_platform: 'tongyi'  #Optional platforms: 'ollama', 'tongyi', 'spark', 'qianfan', 'openrouter'
   

3.2 Starting and Testing the Function

1. Prepare the image file:

   Place the image file to be tested in the following path:

   ~/yahboom_ws/src/largemodel/resources_file/visual_positioning

   Then name the image test_image.jpg

2. Start the largemodel main program:

   Open a terminal, enter the Docker container, and run the following command:

   xxxxxxxxxx
   ros2 launch largemodel largemodel_control.launch.py text_chat_mode:=true
   

3. Send text command: Open another terminal, enter the same Docker container, and run the following command:

   xxxxxxxxxx
   ros2 run text_chat text_chat
   

   Then start typing: "Analyze the position of the dinosaur in the image."

4. Observe the results: In the first terminal where the main program is running, you will see log output indicating that the system received the command, called the visual_positioning tool, completed the execution, and saved the coordinates to a file.

   This file can be found in the ~/yahboom_ws/src/largemodel/resources_file/visual_positioning directory.