Robot Waypoint Mapping With ICP Localization for GPS-Free Traversal
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current autonomous map traversal systems for robots lack efficient methods to accurately navigate and localize within environments without global positioning information, relying heavily on beacon navigation and requiring extensive optimization of sensor data for accurate waypoint placement and edge annotations.
Innovation Solution
The method involves using data processing hardware to receive sensor data, including image data from 3D volumetric sensors and inertial measurements, to execute waypoint heuristics for placing waypoints on a map, associating these waypoints with pose transforms and spatial features, and employing the iterative closest points (ICP) algorithm for localization, allowing robots to traverse environments autonomously and adapt to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If beacon navigation is used for autonomous map traversal, then robots can navigate without global positioning, but the system requires extensive optimization of sensor data and additional navigation aids
Solution Approach 1:
The patent extracts and removes the requirement for global positioning systems and extensive sensor optimization by implementing a beacon-based navigation system that uses only minimal sensor data. The beacon navigation approach isolates the core navigation function from complex sensor processing requirements, allowing robots to navigate autonomously using simple beacon detection and placement mechanisms.
2Measurement precision
If extensive sensor data optimization is performed for accurate waypoint placement, then waypoint accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by performing only the minimum necessary sensor data processing required for accurate waypoint placement using beacon navigation. Instead of extensively optimizing all sensor data, the system selectively processes only the critical beacon-related sensor information, achieving sufficient waypoint accuracy without the computational overhead of comprehensive sensor optimization.
3Reliability
If additional navigation aids are deployed to improve localization accuracy, then navigation reliability improves, but system complexity and cost increase
Solution Approach 1:
The patent implements self-service by enabling the robot to perform localization and navigation using its own onboard sensors and beacon detection capabilities, without requiring additional external navigation aids. The beacon navigation system allows the robot to autonomously place beacons and use them for localization, making the system self-sufficient and eliminating the need for complex additional navigation infrastructure.
Data Source
AI summary
A robot includes a drive system configured to maneuver the robot about an environment and data processing hardware in communication with memory hardware and the drive system. The memory hardware stores instructions that when executed on the data processing hardware cause the data processing hardware to perform operations. The operations include receiving image data of the robot maneuvering in the environment and executing at least one waypoint heuristic. The at least one waypoint heuristic is configured to trigger a waypoint placement on a waypoint map. In response to the at least one waypoint heuristic triggering the waypoint placement, the operations include recording a waypoint on the waypoint map where the waypoint is associated with at least one waypoint edge and includes sensor data obtained by the robot. The at least one waypoint edge includes a pose transform expressing how to move between two waypoints.