Robot Elevation Change Detection Using Camera-LiDAR Fusion
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Solution Overview
Problem
Robots lack the capability to efficiently detect and navigate elevation changes such as stairs, curbs, and cliffs in human-centered environments, often requiring expensive sensors and infrastructure modifications, and existing algorithms are limited to either ascending or descending scenarios.
Innovation Solution
An elevation change detection system for robots using a sensor suite comprising a camera and LiDar sensor, which fuse data to detect edges of structures and adjust the robot's operating system in real-time for efficient traversal, eliminating the need for external sensors and reducing computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are placed outside the robot to detect elevation changes, then detection capability is improved, but infrastructure changes are required and the robot cannot operate independently
Solution Approach 1:
The robot equips itself with sensor suites (cameras, LIDAR, depth sensors) mounted on its body, allowing it to independently detect and process elevation change information without relying on external infrastructure. The robot's own sensors and processing systems serve the detection function, enabling autonomous operation in diverse environments.
Solution Approach 2:
The robot's sensor suite performs multiple functions: detecting elevation changes, mapping environments, navigating obstacles, and recognizing structures. This multi-functional approach eliminates the need for specialized external sensors while enhancing the robot's adaptability to various environments.
2Measurement precision
If sophisticated algorithms and expensive sensors are used to detect elevation changes, then detection accuracy is improved, but system complexity and cost increase
Solution Approach 1:
The system combines multiple sensor types (cameras, LIDAR, depth sensors) into an integrated sensor suite that shares processing resources and data fusion pipelines. This merging approach improves detection accuracy through multi-sensor data fusion while reducing overall system complexity compared to separate specialized systems.
Solution Approach 2:
The sensor suite and processing algorithms serve multiple purposes including elevation detection, environment mapping, obstacle recognition, and navigation. This multi-functionality reduces the need for specialized expensive sensors while maintaining high detection accuracy through versatile data processing.
3Device complexity
If existing algorithms are designed for specific cases (ascending or descending), then algorithm simplicity is maintained, but versatility across different elevation scenarios is limited
Solution Approach 1:
The processing system employs a unified algorithm framework that handles multiple elevation scenarios (ascending stairs, descending stairs, curbs, cliffs, ramps) through a single versatile detection pipeline. The same sensor suite and processing algorithms adapt to different scenarios by analyzing geometric features and elevation patterns, eliminating the need for separate specialized algorithms.
Solution Approach 2:
The algorithm dynamically adapts its parameters and processing steps based on the detected scenario type. The system adjusts its interpretation of sensor data and control responses according to whether it detects ascending or descending terrain, enabling a single algorithm to handle diverse elevation changes effectively.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables robots to traverse elevation changes with higher accuracy and stability by continuously scanning and adjusting movements in real-time, reducing the likelihood of falling and enhancing autonomy in various environments.
Implementation Method 1
a sensor suite including at least one of the camera and the LiDar sensor
Data Source
AI summary
An elevation change detection system is provided for a robot having an operating system. The elevation change detection system includes a sensor, a processor electrically connected to the sensor, and a memory. The memory has instructions that, when executed by the processor, cause the processor to perform operations including detect an edge of a structure with the sensor, classify an elevation change associated with the edge, and adjust the operating system based on the elevation change.