ShelfAware: Real-Time Semantic Localization in Quasi-Static Environments with Low-Cost Sensors
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917549905543168 |
|---|---|
| author | Agrawal, Shivendra Brawer, Jake Naik, Ashutosh Roncone, Alessandro Hayes, Bradley |
| author_facet | Agrawal, Shivendra Brawer, Jake Naik, Ashutosh Roncone, Alessandro Hayes, Bradley |
| contents | Many indoor workspaces are quasi-static: their global geometric layout is stable, but local semantics change continually, producing repetitive geometry, dynamic clutter, and perceptual noise that defeat standard vision-based localization. We present ShelfAware, a semantic particle filter for robust global localization that treats scene semantics as statistical evidence over object categories rather than fixed quantity landmarks. ShelfAware fuses a depth likelihood with a category-centric semantic similarity and uses a precomputed bank of semantic viewpoints to perform inverse semantic proposals inside Monte Carlo Localization (MCL), yielding fast, targeted hypothesis generation on low-cost, vision-only hardware. To demonstrate perception-agnostic scalability, we evaluate ShelfAware across two domains. In a rigorously controlled mock retail environment, ShelfAware achieves a 97% global localization success rate, maintaining the highest tracking success (66%) across cart, wearable, and dynamic occlusion conditions. Furthermore, in a 3,500 sq. ft. operational grocery store leveraging an open-vocabulary vision pipeline, ShelfAware significantly outperforms both geometric and fixed-quantity semantic baselines. By modeling semantics distributionally and leveraging inverse proposals, ShelfAware resolves geometric aliasing, providing an infrastructure-free building block for mobile and assistive robots in dynamic real-world environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_09065 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ShelfAware: Real-Time Semantic Localization in Quasi-Static Environments with Low-Cost Sensors Agrawal, Shivendra Brawer, Jake Naik, Ashutosh Roncone, Alessandro Hayes, Bradley Robotics Artificial Intelligence Many indoor workspaces are quasi-static: their global geometric layout is stable, but local semantics change continually, producing repetitive geometry, dynamic clutter, and perceptual noise that defeat standard vision-based localization. We present ShelfAware, a semantic particle filter for robust global localization that treats scene semantics as statistical evidence over object categories rather than fixed quantity landmarks. ShelfAware fuses a depth likelihood with a category-centric semantic similarity and uses a precomputed bank of semantic viewpoints to perform inverse semantic proposals inside Monte Carlo Localization (MCL), yielding fast, targeted hypothesis generation on low-cost, vision-only hardware. To demonstrate perception-agnostic scalability, we evaluate ShelfAware across two domains. In a rigorously controlled mock retail environment, ShelfAware achieves a 97% global localization success rate, maintaining the highest tracking success (66%) across cart, wearable, and dynamic occlusion conditions. Furthermore, in a 3,500 sq. ft. operational grocery store leveraging an open-vocabulary vision pipeline, ShelfAware significantly outperforms both geometric and fixed-quantity semantic baselines. By modeling semantics distributionally and leveraging inverse proposals, ShelfAware resolves geometric aliasing, providing an infrastructure-free building block for mobile and assistive robots in dynamic real-world environments. |
| title | ShelfAware: Real-Time Semantic Localization in Quasi-Static Environments with Low-Cost Sensors |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2512.09065 |