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NeuSE system enhances object-based SLAM with SE(3)-equivariant embeddings

By PulseAugur Editorial · [1 sources] ·

Researchers have developed NeuSE, a novel neural embedding system designed for object-based Simultaneous Localization and Mapping (SLAM). This system creates latent object embeddings that capture an object's shape, scale, and transform information, enabling robust spatial understanding even with long-term scene changes. NeuSE is SE(3) and scale equivariant, allowing it to generalize effectively to objects of varying sizes and poses. The system can directly infer SE(3) camera pose constraints, which can be integrated with existing SLAM pipelines to improve localization and maintain change-aware mapping. AI

IMPACT This research could improve the robustness and adaptability of robotic systems in complex, dynamic environments.

RANK_REASON The cluster contains a research paper detailing a new technical approach. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

NeuSE system enhances object-based SLAM with SE(3)-equivariant embeddings

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The cluster contains a research paper detailing a new technical approach. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jiahui Fu, Yilun Du, Kurran Singh, Joshua B. Tenenbaum, John J. Leonard ·

    NeuSE: Neural SE(3)-Equivariant Embedding for Long-Term Object-Based Simultaneous Localization and Mapping

    arXiv:2303.07308v3 Announce Type: replace-cross Abstract: We present NeuSE, a novel Neural SE(3)-Equivariant Embedding for objects, and illustrate how it supports object-based Simultaneous Localization and Mapping (SLAM) for consistent spatial understanding with long-term scene c…

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