Spatio-Temporal Model for Multi-Robot Object Pose Tracking
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Solution Overview
Problem
Existing robotic systems face limitations in detecting objects outside their sensor field of view and suffer from noisy sensor inputs due to obstacles or distance, and curated maps may be outdated or lack granularity, failing to represent new or removed objects accurately.
Innovation Solution
A spatio-temporal model is generated using observations from multiple robots, defining pose values and corresponding times for objects in the environment, incorporating uncertainty measures to filter and assign confidence levels to pose values, allowing for accurate object localization and tracking over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor field of view is limited, then device complexity is reduced, but measurement precision deteriorates because objects outside line of sight cannot be detected
Solution Approach 1:
The patent combines data from multiple robots with different sensor fields of view to create a comprehensive environmental model. By merging observations from multiple sources, the system achieves complete object detection without requiring each individual robot to have omnidirectional sensors, thus improving measurement precision while maintaining reasonable device complexity.
Solution Approach 2:
The spatio-temporal model serves multiple functions: it tracks object positions, estimates poses, predicts future states, and provides uncertainty quantification. This multi-functional approach allows a single data structure to replace multiple specialized systems, improving detection capability without proportionally increasing complexity.
2Measurement precision
If sensor distance to object increases, then device complexity is reduced, but measurement precision deteriorates due to noisy sensor input
Solution Approach 1:
The system combines pose measurements from multiple robots observing the same object from different distances. By merging data where some robots are far and others are near, the system achieves accurate pose estimation without requiring all sensors to be close to objects, thus improving precision while maintaining operational flexibility.
Solution Approach 2:
The uncertainty estimation mechanism provides feedback about measurement quality. When a robot detects high uncertainty in its pose measurements (due to distance or occlusion), the system can request additional observations from other robots or adjust its confidence in the current estimate, thereby maintaining precision without forcing all sensors to operate at optimal distances.
3Measurement precision
If curated map granularity is reduced, then device complexity is reduced, but measurement precision deteriorates because certain objects are not represented
Solution Approach 1:
The spatio-temporal model dynamically adapts its granularity based on observed objects and their importance. Rather than using a fixed high-resolution map of the entire environment, the system maintains detailed representations only for objects that are currently observed or relevant to tasks, automatically adjusting the level of detail as objects enter and leave sensor fields of view.
Solution Approach 2:
The system applies different levels of representation detail to different regions and objects based on their relevance. High-granularity representations are maintained for objects that are currently observed or task-critical, while low-granularity or no representations are used for distant or irrelevant areas, optimizing precision where needed while reducing overall complexity.
4Reliability
If curated map is updated frequently, then reliability is improved, but loss of time increases due to continuous environmental monitoring
Solution Approach 1:
The system performs map updates periodically based on trigger events such as when objects enter or leave sensor fields of view, rather than continuously monitoring all environmental changes. This event-driven periodic updating maintains reliability by capturing significant environmental changes while minimizing unnecessary processing time.
Solution Approach 2:
The spatio-temporal model dynamically determines when updates are necessary based on observed changes in object positions and environmental conditions. The system adapts its updating frequency to the actual rate of environmental change, maintaining high reliability during dynamic periods while reducing processing time during stable periods.
Data Source
AI summary
Methods, apparatus, systems, and computer-readable media are provided for generating and using a spatio-temporal model that defines pose values for a plurality of objects in an environment and corresponding times associated with the pose values. Some implementations relate to using observations for one or more robots in an environment to generate a spatio-temporal model that defines pose values and corresponding times for multiple objects in the environment. In some of those implementations, the model is generated based on uncertainty measures associated with the pose values. Some implementations relate to utilizing a generated spatio-temporal model to determine the pose for each of one or more objects an environment at a target time. The pose for an object at a target time is determined based on one or more pose values for the object selected based on a corresponding measurement time, uncertainty measure, and/or source associated with the pose values.