Portable Apparatus Localisation via Path Memory Encoding
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Solution Overview
Problem
Experience-based navigation systems face computational inefficiencies due to the need to handle numerous visual representations of changing environments, leading to degraded localisation performance over time, especially in resource-constrained systems.
Innovation Solution
The implementation of a 'path memory' concept that encodes localisation history into the experience map, allowing for prioritization of relevant experiences and predictive learning to minimize computation during localisation, enabling long-term autonomy on robots with limited resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system accumulates more experiences to handle appearance changes, then localisation robustness improves, but computational workload increases
Solution Approach 1:
The system pre-computes and stores experience data in an experience map during exploration phases, organizing visual memories by location and environmental conditions. This preliminary organization allows the localisation system to quickly retrieve relevant experiences without performing heavy computation during active localisation, thus maintaining robustness while improving computational efficiency during operation
Solution Approach 2:
The experience map is segmented into discrete experiences representing different locations and environmental conditions. Each experience is stored as a separate unit with associated metadata, allowing the system to selectively retrieve only relevant experiences for current localisation needs rather than processing all accumulated experiences, thereby reducing computational workload while maintaining accuracy
2Adaptability or versatility
If the system stores more experiences in the map, then the ability to handle environmental changes improves, but the computational resources required for localisation increase
Solution Approach 1:
The experience map stores experiences with location-specific and condition-specific characteristics. Each experience is tagged with metadata indicating its environmental conditions (lighting, weather, time of day) and location context. During localisation, the system retrieves only experiences matching current conditions, allowing high adaptability to environmental changes while keeping computational resources focused on relevant data only
Solution Approach 2:
The system pre-organizes experiences in the experience map with structured metadata and indexing during exploration phases. This preliminary structuring enables efficient filtering and retrieval based on environmental conditions during localisation, allowing the system to maintain a large diverse experience database without proportionally increasing computational complexity during operation
3Measurement precision
If the system uses more experiences for localisation, then accuracy improves, but the time required for localisation increases
Solution Approach 1:
The system pre-computes feature descriptors and organizes experiences in the experience map during off-line exploration phases. Visual features are extracted and stored in advance with their associated experiences, allowing the localisation system to perform fast matching operations during active localisation without performing heavy feature extraction in real-time, thus maintaining accuracy while reducing localisation time
Solution Approach 2:
The localisation process is segmented into multiple stages: first retrieving a small set of candidate experiences from the experience map based on current conditions and pose estimate, then performing detailed comparison only with these candidates. This segmentation allows the system to maintain high accuracy through thorough comparison while minimizing total localisation time by limiting expensive operations to a small candidate set rather than all experiences
Data Source
AI summary
A method of localizing portable apparatus (200) obtains: a stored experience data set comprising a set of connected nodes; captured location representation data provided by at least one sensor associated with the portable apparatus, and a current pose estimate of the portable apparatus within the environment. The pose estimate is used to select a candidate set of the nodes that contain a potential match for the captured location representation data. The pose estimate is used to obtain a set of paths from path memory data, each said path comprising a set of said nodes previously traversed in the environment under similar environmental/visual conditions. The set of paths is used to refine the candidate set. The captured location representation data and the refined candidate set of nodes is compared in order to identify a current pose of the portable apparatus within the environment.