Mobile Platform Pose Estimation via Sensor Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current mobile robots face challenges in navigating dynamic environments with clutter or restricted spaces due to obstructed line of sight and reliance on expensive, large, or resource-intensive localization equipment, which limits their ability to operate efficiently and safely.
Innovation Solution
A method and apparatus that generate pose estimates for mobile platforms using onboard data systems, combining data streams with uncertainty measurements and pseudo-deterministic data streams through Bayesian estimation, allowing for accurate localization and movement within environments without the need for extensive external sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physical landmarks (paint, tape, magnets) are used to help mobile robot navigate, then the robot can follow pre-defined routes, but the robot is constrained to only follow pre-defined routes and installation is time-consuming and expensive
Solution Approach 1:
The mobile robot performs self-localization using onboard sensors (cameras, LIDAR, IMU) to identify its position and orientation in the environment without relying on external physical landmarks. The robot processes sensor data to generate pose estimates autonomously, eliminating the need for pre-installed navigation aids and enabling flexible movement throughout the environment.
2Measurement precision
If external sensor systems are used for localization, then the mobile robot can identify its pose, but line of sight may be obstructed by objects, robots, and persons causing the robot to stop receiving pose updates
Solution Approach 1:
The system uses multiple sensor modalities (visual features, LIDAR range data, odometry information) as intermediaries to estimate pose when direct external sensor measurement is blocked. These complementary sensors provide alternative pathways for localization, maintaining accuracy even when line of sight to external sensors is obstructed by objects, other robots, or persons.
3Measurement precision
If advanced localization equipment is used to achieve desired accuracy, then the localization precision is improved, but the equipment becomes more expensive, larger, and heavier
Solution Approach 1:
The system combines multiple conventional sensors (camera, LIDAR, IMU, odometry) that are already present on mobile robots into an integrated localization system. By fusing data from these sensors through probabilistic methods, the system achieves high localization accuracy without requiring specialized expensive equipment, thereby reducing device complexity while maintaining measurement precision.
4Measurement precision
If processing is increased to achieve desired localization accuracy, then the localization precision is improved, but the processing becomes more time-consuming and requires more processing resources
Solution Approach 1:
The system pre-computes probability distributions and covariance matrices for sensor measurements, and uses pre-built environmental maps and feature databases. During operation, the localization algorithm efficiently queries these pre-computed structures rather than calculating everything from scratch, significantly reducing real-time processing requirements while maintaining high localization accuracy.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method and apparatus for guiding a mobile platform within an environment may be provided. A number of first type of data streams and a number of second type of data streams may be generated using a plurality of data systems. A probability distribution may be applied to each of the number of second type of data streams to form a number of modified data streams. The number of first type of data streams and the number of modified data streams may be fused to generate a pose estimate with a desired level of accuracy for the mobile platform with respect to an environment around the mobile platform.