Depth Estimation to Enable Autonomous Robot Navigation

Based on Patent Research | US-2023359220-A1 (2023)

General merchandise stores face a challenge in enabling robots to autonomously navigate complex, changing environments. Current reliance on manual map creation or static data limits their operational utility. Depth Estimation, a computer vision technique, provides a solution by allowing robots to perceive distances to objects. This helps robots build accurate 3D environmental representations. It ensures more efficient and safer autonomous navigation within dynamic store layouts.

SOLUTION OVERVIEW

From Traditional to Smart Navigation

For general merchandise stores, enabling robots to navigate complex, changing environments autonomously is a key challenge. Depth Estimation technology directly addresses this by allowing robots to perceive distances to objects. This process begins with sensors gathering visual data, which is then processed to generate a detailed depth map. This map provides a real-time, three-dimensional understanding of the store, allowing robots to build and continually update environmental representations automatically. This capability overcomes the limitations of static maps, ensuring efficient and safer autonomous movement.

This computer vision technique significantly enhances operational intelligence, enabling robots to adapt seamlessly to shifting inventory and customer flows. Depth Estimation integrates with existing robotic platforms, automating the crucial task of environmental mapping. It operates much like how a shopper instinctively gauges the distance to a display to avoid bumping into it. By providing robots with this continuous spatial awareness, general merchandise stores can achieve substantial operational improvements, optimize resource allocation, and support more flexible store layouts, ultimately unlocking greater utility from their autonomous fleets.

HOW IT WORKS

Image Analysis = Depth Understanding

Capturing Store Visual Data

Robot sensors actively collect visual information from the store environment, much like human eyes perceiving their surroundings. This continuous stream of images and video forms the raw input for the AI system to begin understanding the dynamic store layout and its contents.

Estimating Object Distances

The AI system then processes this visual data to precisely calculate the exact distance to every object and surface within the robot's view. This crucial step generates a detailed depth map, which is a real-time, pixel-by-pixel representation of spatial relationships throughout the store.

Building Dynamic 3D Maps

Using the generated depth maps, the system continuously constructs and updates a comprehensive three-dimensional model of the entire store. This dynamic 3D map accurately reflects current inventory placements, customer movements, and any physical layout changes in real-time.

Guiding Autonomous Navigation

Robots leverage this continually updated 3D map to navigate complex store aisles safely and with high efficiency. This spatial awareness allows them to adapt seamlessly to shifting environments, avoid obstacles, and optimize their routes, greatly enhancing their operational utility.

Potential Benefits

Real-time Environmental Awareness

Depth Estimation allows robots to continuously perceive distances to objects, creating real-time 3D maps of the store. This capability enables autonomous navigation that adapts instantly to dynamic layouts and shifting inventory.

Increased Operational Efficiency

By automating environmental mapping and providing precise spatial data, robots can perform tasks more effectively and reliably. This optimizes resource allocation and unlocks greater utility from autonomous fleets.

Enhanced Robot Safety

Robots gain a superior understanding of their surroundings, accurately gauging distances to avoid collisions with people and objects. This ensures safer navigation in busy general merchandise store environments.

Flexible Store Layouts Supported

The system enables robots to adapt seamlessly to changes in store configuration, such as new displays or rearranged aisles. This allows general merchandise stores to implement flexible layouts without extensive reprogramming.

Implementation

1 Install Robot Sensors. Equip autonomous robots with appropriate visual sensors and computing units to capture real-time store data.

2 Deploy AI Software. Install the depth estimation AI model onto the robot's onboard processing unit or a connected edge device.

3 Initial Environment Scan. Conduct an initial scan to generate the foundational 3D map of the store layout and key fixtures.

4 Integrate Navigation System. Connect the real-time depth maps and dynamic 3D environment data with the robot's autonomous navigation software.

5 Monitor and Optimize. Continuously monitor robot navigation, update maps automatically, and refine parameters for enhanced accuracy and safety.

Source: Analysis based on Patent US-2023359220-A1 "Autonomous Map Traversal with Waypoint Matching" (Filed: November 2023).