ADM-Fusion: Adaptive Deep Multi-Sensor Fusion for Robust Ego-Motion Estimation in Diverse Conditions
Proposes ADM-Fusion, an end-to-end deep learning multi-sensor fusion method using an adaptive sensor mixture-of-experts framework with content-aware routing to dynamically weigh sensor inputs. It features separate translation and rotation branches coupled via cross-task attention. Trained on CARLA-LOC simulated dataset and fine-tuned on KITTI real-world data, it demonstrates robust performance under sensor degradation while matching state-of-the-art methods.
[2606.25111] ADM-Fusion: Adaptive Deep Multi-Sensor Fusion for Robust Ego-Motion Estimation in Diverse Conditions
[Submitted on 23 Jun 2026]
Title:ADM-Fusion: Adaptive Deep Multi-Sensor Fusion for Robust Ego-Motion Estimation in Diverse Conditions
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Abstract:Robust multi-sensor fusion is essential for reliable autonomy in diverse and degraded environments, where sensor reliability can fluctuate rapidly. Because different modalities fail in distinct ways, effective fusion should adaptively balance complementary cues rather than rely on fixed weighting. This adaptability is particularly important for ego-motion estimation, since accurate updates depend on the consistent integration of complementary sensor information. We propose ADM-Fusion, an end-to-end deep learning based multi-sensor fusion method designed to adapt to environmental changes and sensor degradation. ADM-Fusion employs an adaptive sensor mixture-of-experts framework with content-aware routing to dynamically assign weights to sensor inputs in real time. The system further incorporates separate translation and rotation branches, coupled through a cross-task attention mechanism to preserve task-specific specialization while enabling information sharing. ADM-Fusion is trained on the CARLA-LOC simulated dataset and subsequently fine-tuned on KITTI real-world data, demonstrating effective simulation-to-real transfer. Experiments show that ADM-Fusion remains robust under degraded conditions while maintaining competitive performance against existing methods.
Comments: 8 pages, 4 figures
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.25111 [cs.RO]
(or arXiv:2606.25111v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.25111
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Hasan Moughnieh [view email] [v1] Tue, 23 Jun 2026 19:38:28 UTC (3,297 KB)
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