SLAM in Low-Light Environments: Project Report
Simultaneous localization and mapping (SLAM) is one of the fundamental problems in robotics, as it enables autonomous operations in real-world scenarios. Under low illumination, reduced contrast, sensor noise, and motion blur degrade both feature extraction and feature matching, while compensating with LiDAR, depth, or thermal sensors raises cost, power draw, and integration complexity. Existing benchmarks remain dominated by well-lit indoor or daylight sequences, leaving open how far SLAM with
Record details
Published: 19 July 2026
Source: HuggingFace Daily Papers
Category: Research
Topics: Agents & autonomy · Environment
Retrieved: 25 July 2026
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ethics.ai (19 July 2026), “SLAM in Low-Light Environments: Project Report,” evidence record 13027, https://ethics.ai/record/13027 (originally published by HuggingFace Daily Papers).
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