Spatial Recognition System Unifies Multi-Device Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional spatial recognition systems face challenges in accurately estimating self-location, particularly in environments with interrupted navigation signals, such as buildings or tunnels, and suffer from increased errors with distance from reference markers, leading to instability and loss of self-location estimation.
Innovation Solution
A spatial recognition system that utilizes multiple devices to generate and unify spatial data in a shared coordinate system, allowing for precise self-location estimation and sharing of location information between devices, with mechanisms for correcting errors and maintaining stability through data unification and self-location sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GNSS is used for self-location estimation, then outdoor positioning is achievable, but the system cannot be used in locations where navigation signals are interrupted such as buildings or tunnels
Solution Approach 1:
The system segments the positioning function into multiple independent components: GNSS for outdoor positioning, marker-based positioning for indoor/covered areas, and visual odometry for continuous tracking. Each component operates independently and can be selected based on environmental conditions, allowing the system to maintain reliability across diverse environments including tunnels and buildings where GNSS signals are interrupted
Solution Approach 2:
The spatial recognition device is designed with multi-functional positioning capabilities, incorporating both GNSS receiver and marker recognition functions within a single device. This universal positioning system can automatically switch between satellite-based and marker-based methods depending on signal availability, enabling the device to operate reliably in both outdoor open spaces and indoor/covered environments without requiring separate positioning systems
2Adaptability or versatility
If a marker is used as reference for self-location estimation, then the system can work in indoor environments, but the error increases as the device moves farther from the marker
Solution Approach 1:
Visual odometry serves as an intermediary mechanism that bridges the gap between marker-based positioning points. By continuously tracking visual features in the environment and calculating relative motion, the system can maintain accurate self-location estimation even when far from markers or during transitions between marker zones, effectively compensating for the distance-related precision loss inherent in marker-based systems
Solution Approach 2:
The system merges marker-based positioning with visual odometry to create a hybrid positioning approach. The marker provides absolute position reference when nearby, while visual odometry provides continuous relative position tracking. By combining these methods, the system maintains high precision both near markers and at distances, as the two methods complement each other's strengths and weaknesses
3Measurement precision
If a total station is used to follow a prism on the worker's hard hat, then positioning precision is improved, but the location cannot be recognized when the device deviates from the visual field of the total station
Solution Approach 1:
The device performs self-positioning through visual odometry by independently detecting and tracking visual features in the environment. This self-service capability eliminates the need for external total station equipment to maintain continuous visual contact, allowing the worker to move freely without constraint while maintaining positioning accuracy through autonomous visual feature tracking and relative position calculation
Solution Approach 2:
The positioning function is segmented into independent operational modes: total station mode for high-precision work when within visual range, and visual odometry mode for free movement when outside visual range. The system can switch between these segmented functions based on availability, maintaining both precision and movement freedom by using the appropriate method for each situation
4Ease of operation
If single-device spatial recognition is used, then the system is simple to operate, but incidental failures in self-location estimation easily cause problems in content projection
Solution Approach 1:
Multiple devices provide mutual feedback on their self-location estimates and spatial data. When one device experiences a failure or loss in position estimation, other devices can detect this through lack of feedback and compensate by providing their own location data or alerting the system to switch to alternative positioning methods, thereby maintaining content projection reliability without complicating the operational interface
Solution Approach 2:
The system implements beforehand cushioning by having multiple devices ready to provide backup positioning support. Before a failure occurs, redundant devices are already positioned and operational, so when one device fails, the system can immediately rely on the pre-positioned backup devices to maintain accurate content projection, preventing problems before they occur rather than reacting after failure
Data Source
AI summary
A spatial recognition device has a spatial recognition unit for generating spatial data by recognizing a three-dimensional shape in a real space, and a self-location estimating unit for estimating the self-location in real space. A spatial data unification unit unifies the spatial data and spatial data generated by another spatial recognition device to generate unified spatial data that is expressed in the same coordinate system having the same origin. A self-location sharing unit transmits the self-location based on the unified spatial data, to another spatial recognition device. The self-location sharing unit acquires the self-location from another spatial recognition device) when the self-location estimating unit cannot estimate the self-location.