Exploring Depth Estimation for Improved Robot Navigation

Based on Patent Research | US-11747825-B2 (2023)

Maintaining accurate maps for robots in large general merchandise stores is difficult. Current methods for creating these maps are time-consuming and need expert knowledge. Depth estimation, a computer vision task, helps robots understand the distance to objects. This allows them to build 3D maps from sensor data and navigate autonomously. This technology improves efficiency, reduces mapping costs, and enables robots to adapt to changing store layouts, similar to how humans perceive spatial relationships.

SOLUTION OVERVIEW

Manual Mapping Transformed by AI

Depth estimation offers a solution for general merchandise stores seeking to improve robotic navigation. By analyzing visual data, this technology allows robots to perceive their surroundings and understand the distance to objects. The robot captures sensor data, processes it using computer vision algorithms, and generates a 3D map of the store. This enables autonomous movement and adaptation to the store's layout, enhancing operational efficiency.

This technology automates map creation and updates, integrating with existing robotic systems. For example, imagine a store employee who can instantly perceive the layout of an entire aisle, including the distance to each shelf. Depth estimation provides robots with a similar capability, allowing them to optimize tasks like inventory scanning and shelf restocking. This leads to significant operational improvements and resource optimization, offering enhanced adaptability within the retail environment.

HOW IT WORKS

Transforming Images to Depth Maps

Capturing Store Environment Visually

Capturing visual data is the initial step. The robot uses its sensors, such as cameras, to collect images and video of the general merchandise store environment. This raw visual input forms the basis for subsequent depth estimation.

Analyzing Images for Depth

Analyzing images to perceive depth comes next. The system employs computer vision algorithms to process the captured images, identifying features and patterns that indicate distance. This analysis mimics how humans perceive depth using their eyes.

Generating a 3D Store Map

Generating a 3D map of the store is the crucial output. Based on the depth information extracted from the images, the system constructs a detailed 3D map of the store's layout, including shelves, aisles, and products. This map allows the robot to understand its surroundings and navigate effectively.

Navigating Autonomously in the Store

Navigating autonomously using the generated map is the final step. With the 3D map, the robot can plan its routes and move independently throughout the store. This enables tasks like inventory scanning and shelf restocking without human guidance, optimizing store operations.

Potential Benefits

Enhanced Adaptability to Store Layouts

Robots can now quickly adapt to store layout changes, eliminating delays caused by manual mapping updates. This ensures smooth operations and minimizes downtime, leading to better resource utilization.

Reduced Mapping and Labor Costs

The AI automates map creation, removing the need for specialized expertise or manual labor. This significantly reduces mapping costs and frees up staff for other critical tasks.

Improved Accuracy and Consistency

Depth estimation provides robots with accurate spatial awareness, improving navigation and task execution. This leads to more consistent and reliable performance in inventory scanning and shelf restocking.

Enhanced Operational Efficiency

By understanding the store environment in 3D, robots can optimize routes and workflows. This improved efficiency translates to faster task completion and optimized resource allocation.

Implementation

1 Sensor Installation. Install Robot Sensors, ensuring optimal placement for comprehensive visual data capture throughout the store.

2 System Calibration. Calibrate Depth Estimation, adjusting parameters for accurate distance perception in the store environment.

3 Initial Mapping. Map Initial Store Layout, creating a baseline 3D map for the robot's navigation.

4 System Integration. Integrate with Robotics System, connecting the depth data to the robot's navigation system.

5 Navigation Testing. Test Autonomous Navigation, verifying the robot's ability to navigate and adapt to changing store layouts.

Source: Analysis based on Patent US-11747825-B2 "Autonomous map traversal with waypoint matching" (Filed: September 2023).