3D Depth Camera Navigation for Floor Obstacle Detection
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Solution Overview
Problem
Autonomous mobile robots face challenges in navigating indoor environments with unstructured layouts and untrained personnel due to the complexity of obstacle detection and avoidance, especially when operating at significant heights, as existing sensors provide inadequate or costly solutions that fail to accurately detect obstacles like slopes, cliffs, and holes.
Innovation Solution
Employing two three-dimensional depth cameras mounted on the robot body to provide a 360-degree field of view, combined with a processing system that analyzes pixel data to distinguish between obstacles, shadows, and the robot's body by comparing against a template, enabling precise obstacle detection and navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional obstacle sensors are used for navigation, then obstacle detection capability is provided, but measurement precision is inadequate for detecting slopes, cliffs, and holes
Solution Approach 1:
The patent combines multiple depth camera sensors (at least two 3D depth cameras) to create a comprehensive depth map of the environment. By merging data from multiple sensors positioned at different locations on the robot body, the system achieves high-precision detection of various obstacle types including slopes, cliffs, and holes, resolving the contradiction between measurement precision and navigation safety.
Solution Approach 2:
The patent transitions from 2D image data to 3D depth information by using depth cameras that capture depth maps. This dimensional change enables precise detection of obstacle height, slope, and depth characteristics, allowing the robot to distinguish between different types of obstacles (slopes, cliffs, holes) with high measurement precision for safe navigation.
2Area of stationary object
If sensor arrays are used for obstacle detection, then detection coverage is improved, but device complexity and cost increase
Solution Approach 1:
The patent uses depth cameras that serve multiple functions: they provide wide-field-of-view coverage, generate depth maps for obstacle detection, identify different obstacle types (slopes, cliffs, holes), and support navigation planning. This multi-functionality achieves comprehensive detection coverage without requiring separate specialized sensors for each function, thereby reducing device complexity.
Solution Approach 2:
The patent divides the detection task into processing depth map data to identify different obstacle types based on their geometric characteristics. By segmenting the analysis of depth information, the system achieves comprehensive coverage of various obstacle types using a unified sensor platform, avoiding the need for complex multi-sensor arrays.
3Length of stationary object
If depth camera sensors are mounted at robot height, then obstacle detection range is improved, but difficulty of detecting and measuring increases due to distance from floor
Solution Approach 1:
The patent uses depth cameras mounted on the robot body that capture three-dimensional depth information. By processing depth maps to identify geometric characteristics of obstacles (slopes, cliffs, holes), the system overcomes the challenge of measuring obstacles from height. The depth dimension enables accurate measurement of obstacle properties despite the distance from the floor, resolving the contradiction between detection range and measurement difficulty.
Data Source
AI summary
An apparatus, system and method of operating an autonomous mobile robot having a height of at least one meter. The robot body; at least two three-dimensional depth camera sensors affixed to the robot body proximate to the height, wherein the sensors are directed toward a floor surface and, in combination, comprise a substantially 360 degree field of view of the floor surface around the robot body; and a processing system for receiving pixel data within the field of view of the sensors; obtaining missing or erroneous pixels from the pixel data; comparing the missing or erroneous pixels to a template, wherein the template comprises at least an indication of ones of the missing or erroneous pixels indicative of the robot body and a shadow of the robot body; and outputting an indication of obstacles in or near the field of view based on the comparing.