Autonomous Robot Mapping Enabled by Depth Estimation

Based on Patent Research | CN-118189926-A (2024)

Efficient mapping is crucial for deploying robots in building construction. Current mapping methods can be slow, especially in complex or large areas. This increases both time and resources. Depth estimation, a computer vision task, allows robots to perceive the 3D structure of their environment. With depth estimation, robots can build accurate maps for autonomous navigation. This reduces mapping time and improves robot efficiency on construction sites.

SOLUTION OVERVIEW

Upgrading Manual Maps to AI

For building construction, depth estimation offers a solution to inefficient mapping processes. The technology uses sensors to capture visual data of a construction site. Sophisticated algorithms then process this data to calculate distances, creating a detailed depth map. This map allows robots to understand the 3D structure of their surroundings, enabling autonomous navigation and exploration within the construction environment.

This technology automates the mapping process, integrating with existing robotic systems used on construction sites. Think of it as a surveyor using a theodolite, but instead of manual measurements, a robot autonomously creates the map. Depth estimation enables robots to nimbly adapt to dynamic site conditions. This leads to faster project timelines, reduced labor costs, and more precise robotic operation. Ultimately, this improves construction efficiency and accuracy.

HOW IT WORKS

Images Yield Depth Maps

Capturing Construction Site Imagery

Capturing visual data of the construction site is the first step. Sensors, like cameras or specialized depth sensors, gather images and video. This raw visual information serves as the foundation for creating a depth map.

Analyzing Images for Depth Information

Next, the system processes the collected visual data. Sophisticated algorithms analyze the images to identify key features and spatial relationships. This analysis estimates the distance to various points within the scene.

Generating a 3D Depth Map

The algorithm then generates a detailed depth map of the construction environment. This map represents the distance from the sensor to each point in the scene, creating a 3D representation. This map allows robots to understand the structure of the site.

Guiding Robot Navigation and Exploration

Finally, the depth map is used to guide the robot's navigation and exploration. The robot can understand the layout of the site, identify obstacles, and plan its path accordingly. This improves mapping efficiency and allows for autonomous operation.

Potential Benefits

Accelerated Project Timelines

Faster Mapping and Navigation Depth estimation significantly reduces the time needed to map construction sites. This allows robots to navigate autonomously and efficiently, saving valuable time and resources.

Enhanced Robotic Precision

Improved Accuracy and Consistency By automating the mapping process, depth estimation ensures more precise and consistent data collection. This reduces errors and improves the reliability of robotic operations on construction sites.

Lower Labor Expenses

Reduced Operational Costs Automated depth estimation decreases the need for manual surveying and mapping. This lowers labor costs and optimizes resource allocation within construction projects.

Increased Site Adaptability

Adaptable to Dynamic Environments Depth estimation allows robots to quickly adapt to changing conditions on construction sites. This ensures continuous and efficient operation, even in complex and evolving environments.

Implementation

1 Sensor Installation. Install site sensors, ensuring optimal field of view and secure mounting.

2 System Calibration. Calibrate cameras and depth sensors to maximize accuracy in data collection.

3 Depth Map Generation. Process collected images using depth estimation algorithms to create site maps.

4 Robotic Integration. Integrate depth maps with robot navigation to enable autonomous exploration.

5 Ongoing Monitoring. Continuously monitor and refine depth maps to adapt to site changes.

Source: Analysis based on Patent CN-118189926-A "Map construction method and device" (Filed: June 2024).