Robot Waypoint Mapping for Beacon-Free Autonomous Traversal
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Solution Overview
Problem
Current autonomous map traversal systems for robots lack efficient methods to accurately navigate and localize within environments using sensor data, particularly in complex terrains, without relying on global positioning information or beacons.
Innovation Solution
The system employs a method for autonomous map traversal using data processing hardware to receive sensor data, execute waypoint heuristics based on image data, and record waypoints on a map, including pose transforms and spatial features, enabling the robot to navigate using an iterative closest points (ICP) algorithm for localization and traversal between waypoints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If autonomous map traversal systems use traditional navigation methods with global positioning information or beacons, then localization accuracy is improved, but device complexity and dependency on external infrastructure increase
Solution Approach 1:
The patent extracts the localization function from dependency on external global positioning infrastructure and implements it using only local sensor data (LIDAR, cameras, IMU). The system creates and uses local maps independently, removing the need for external beacons or GPS, thereby reducing system complexity while maintaining localization capability
Solution Approach 2:
The robot performs self-localization by creating and utilizing its own local maps generated from sensor data. The system serves its own localization needs through iterative closest points (ICP) algorithms that match current sensor readings against stored local map data, eliminating dependency on external positioning services
2Adaptability or versatility
If autonomous robots navigate complex terrains without global positioning information, then adaptability to diverse environments is improved, but measurement precision and localization accuracy deteriorate
Solution Approach 1:
The system dynamically adjusts localization parameters by switching between different sensor data types (LIDAR point clouds, camera images, IMU data) and adjusting ICP algorithm parameters based on environmental conditions. This allows maintaining localization accuracy across diverse terrains by optimizing parameters for specific environmental contexts
Solution Approach 2:
Local maps serve as intermediaries between the robot and the environment, capturing spatial features and geometry that mediate the localization process. These maps act as a reference framework that enables accurate positioning in complex terrains without requiring direct global positioning signals
3Manufacturing precision
If the system records detailed sensor data for each waypoint including three-dimensional point cloud data, then map quality and traversal accuracy are improved, but data processing time and memory requirements increase
Solution Approach 1:
The patent segments the environment into discrete waypoints, each containing localized sensor data and features. This segmentation allows the system to process and store data in manageable units, reducing overall processing time while maintaining high map quality through selective detail retention at key locations
Solution Approach 2:
The system records full three-dimensional point cloud data at selected waypoint locations (partial action) rather than continuously throughout the environment. This selective recording maintains map quality at critical positions while significantly reducing total data processing requirements and memory usage
4Measurement precision
If the robot uses iterative closest points (ICP) algorithm for localization at each waypoint, then localization precision is improved, but computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing sensor data into structured local maps with extracted features and geometry information before runtime localization. This preprocessing reduces the computational burden during actual ICP execution, maintaining precision while reducing real-time processing complexity
Solution Approach 2:
The patent applies ICP algorithm with high precision requirements only at specific waypoint locations where accurate localization is critical, rather than continuously throughout navigation. This localized application of complex algorithms maintains necessary precision while reducing overall computational complexity during traversal
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.