LIDAR Point-Cloud Detection of Dropped Objects Around Robots
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Solution Overview
Problem
Robots often fail to detect and grasp objects that have fallen to the ground due to limited perception systems, leading to inefficiencies and potential damage, as conventional techniques require human intervention to identify and clean up dropped objects.
Innovation Solution
A method using a distance-based point cloud from LIDAR sensors to filter and cluster points, identifying objects by determining characteristics such as shape and size, allowing the robot to detect and pick up dropped objects without interrupting its primary tasks and without relying on camera views.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional perception systems (cameras) are used to detect objects, then the robot can identify objects within its field of view, but it cannot detect dropped objects located behind or to the side of the robot
Solution Approach 1:
The patent transitions from 2D camera-based detection to 3D LIDAR point cloud analysis, enabling detection in previously inaccessible spatial dimensions around the robot
Solution Approach 2:
The LIDAR system serves multiple functions: primary navigation, obstacle detection, and dropped object detection, eliminating the need for separate detection systems for different scenarios
2Reliability
If human intervention is used to identify and clean up dropped objects, then all dropped objects can be detected and removed, but the total time a person needs to tend to the robot increases and efficiency decreases
Solution Approach 1:
The robot autonomously detects and retrieves dropped objects using its own LIDAR system and manipulation capabilities, eliminating the need for human intervention and maintaining continuous operation
Solution Approach 2:
The system continuously monitors the environment using LIDAR, provides feedback about dropped objects to the control system, and autonomously executes retrieval actions to maintain operational efficiency
3Device complexity
If the robot uses its primary perception system for both main tasks and dropped object detection, then no additional hardware is needed, but detection accuracy for dropped objects decreases due to limited field of view
Solution Approach 1:
The LIDAR system is designed to perform both primary navigation functions and dropped object detection simultaneously, achieving multi-functionality without increasing hardware complexity
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 efficiently detect and grasp dropped objects autonomously, reducing downtime and preventing damage by integrating distance sensors and computing devices to analyze environmental data and control the robot's actions.
Implementation Method 1
data captured using at least one LIDAR sensor
Data Source
AI summary
Methods and apparatuses for detecting one or more objects (e.g., dropped objects) by a robotic device are described. The method comprises receiving a distance-based point cloud including a plurality of points in three dimensions, filtering the distance-based point cloud to remove points from the plurality of points based on at least one known surface in an environment of the robotic device to produce a filtered distance-based point cloud, clustering points in the filtered distance-based point cloud to produce a set of point clusters, and detecting one or more objects based, at least in part, on the set of point clusters.