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IndoorBEV: A Lightweight Real-Time LiDAR BEV Perception System

1 reports1 reporting sources2 days agoUpdated

Overview

Event synthesis

IndoorBEV is a lightweight LiDAR perception framework for indoor robots, using height-aware BEV and geometry-conditioned fusion for efficient real-time perception.

Generated from attributed reports · 2 days agoUpdated

Event evidence and corrections

0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.

Reported quantity · parameters: 0.6 other · Basis not reported
Supporting report

“IndoorBEV contains only 0.6M parameters and requires 2.3 MB of model storage.”

Exact source · revision 1

Source owner not reported

Reported quantity · model storage: 2.3 other · Basis not reported
Supporting report

“IndoorBEV contains only 0.6M parameters and requires 2.3 MB of model storage.”

Exact source · revision 1

Source owner not reported

Reported quantity · GPU memory: 21.52 other · Basis not reported
Supporting report

“IndoorBEV contains only 0.6M parameters and requires 2.3 MB of model storage.”

Exact source · revision 1

Source owner not reported

Reported quantity · latency: 169.6 other · Basis not reported
Supporting report

“IndoorBEV contains only 0.6M parameters and requires 2.3 MB of model storage.”

Exact source · revision 1

Source owner not reported

Reported quantity · deadline miss ratio: 1.8 percent · Basis not reported
Supporting report

“IndoorBEV contains only 0.6M parameters and requires 2.3 MB of model storage.”

Exact source · revision 1

Source owner not reported

Report timeline

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10/2
  1. arXiv Robotics — research abstracts
    IndoorBEV: A Lightweight Real-Time LiDAR BEV Perception System for Indoor Mobile Robots

    IndoorBEV is a lightweight LiDAR perception framework that addresses the challenge of efficient indoor perception by integrating height-aware BEV representations and geometry-conditioned feature fusion. It achieves 0.6M parameters, 2.3 MB storage, and 169.6 ms latency on an NVIDIA AGX Orin, with a 1.8% deadline miss ratio.

Event attention history

Current attention 3·Peak within the comparable range 9(2026-10-02 06:00 UTC)·Change within the comparable range over 24 hours -49%

02.557.5102026-10-0206:002026-10-0221:002026-10-0312:002026-10-0403:00

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