ZED 2i Overview¶

Depth cameras are a crucial component of any robot. Acting as the robot’s eyes, they enable the robot to not only see objects but also infer their distance from the camera. Unlike standard fisheye cameras, depth cameras like the ZED 2i allow robots to estimate the 3D pose of objects relative to the camera, which is vital for tasks such as navigation and object interaction.

Why Depth Cameras Matter¶

In scenarios where a robot relies heavily on its depth camera:

Object Localization: Depth cameras help estimate the location of objects in the environment while the robot is moving.
Human-Robot Interaction: They enable robots to classify objects and humans, improving situational awareness in dynamic environments like retail stores.
Pose Estimation: The 3D pose of objects can be calculated, helping robots identify the orientation and position of objects.

As shown in the GIF below, the robot classified a handbag with approximately 84% accuracy, demonstrating the capability to detect and classify objects. This was achieved using a trained YOLOv8 model, with data preprocessing and augmentation performed using RoboFlow.

_images/Object_detection_wrt_camera_frame.webp — *3d Object pose detection*¶

Why I Chose YOLOv8¶

I selected the YOLOv8 model for this task because of its flexibility and support for multiple tasks such as detection, classification, and segmentation. Below is a comparison of its key features, which influenced my decision:

YOLOv8 Comparison¶
Feature	YOLOv8	YOLOv5	YOLOv4
Architecture	Unified Detection, Segmentation, Classification	Separate models	Separate models
Speed	Faster than YOLOv5	Moderate	Slower
Accuracy	Improved	High	Moderate
Supported Modes	Detection, Classification, Segmentation	Detection, Classification	Detection only
Ease of Use	Intuitive	Intuitive	Moderate

For more details, refer to the YOLOv8 documentation.

Additionally, the comparison image below illustrates YOLOv8’s performance metrics relative to previous versions, highlighting its superior speed and accuracy.

YOLOv8 Documentation

Note

These attributes make YOLOv8 a clear choice for real-time robotics applications where detection and classification tasks demand both accuracy and efficiency.