Complex sensors such as LiDAR, RADAR, and event cameras have proliferated in autonomous robotics to enhance perception and understanding of the environment. Meanwhile, these sensors are also vulnerable to diverse failure mechanisms that can intricately interact with their operation environment. In parallel, the limited availability of training data on complex sensors also affects the reliability of their deep learning-based prediction flow, where their prediction models can fail to generalize to environments not adequately captured in the training set. To address these reliability concerns, this paper introduces STARNet, a Sensor Trustworthiness and Anomaly Recognition Network designed to detect untrustworthy sensor streams that may arise from sensor malfunctions and/or challenging environments. We specifically benchmark STARNet on LiDAR and camera data. STARNet employs the concept of approximated likelihood regret, a gradient-free framework tailored for low-complexity hardware, especially those with only fixed-point precision capabilities. Through extensive simulations, we demonstrate the efficacy of STARNet in detecting untrustworthy sensor streams in unimodal and multimodal settings. In particular, the network shows superior performance in addressing internal sensor failures, such as cross-sensor interference and crosstalk. In diverse test scenarios involving adverse weather and sensor malfunctions, we show that STARNet enhances prediction accuracy by approximately 10% by filtering out untrustworthy sensor streams. STARNet is publicly available at \url{https://github.com/sinatayebati/STARNet}.

翻译：诸如激光雷达、雷达和事件相机等复杂传感器已在自主机器人领域广泛应用，以增强对环境的感知与理解。然而，这些传感器也易受多种故障机制的影响，这些机制可能与其运行环境产生复杂交互。同时，复杂传感器训练数据的有限可用性也影响了基于深度学习的预测流程的可靠性——其预测模型可能无法泛化到训练集中未充分覆盖的环境。为解决这些可靠性问题，本文提出STARNet——一种传感器可信度与异常识别网络，旨在检测可能由传感器故障和/或恶劣环境引发的不可靠传感器数据流。我们专门在激光雷达和相机数据上对STARNet进行基准测试。STARNet采用近似似然遗憾概念，这是一种专为低复杂度硬件（尤其是仅具备定点精度能力的硬件）设计的无梯度框架。通过大量仿真实验，我们证明了STARNet在单模态和多模态场景中检测不可靠传感器数据流的有效性。该网络在应对内部传感器故障（如交叉传感器干扰和串扰）方面展现出卓越性能。在涉及恶劣天气和传感器故障的多样化测试场景中，STARNet通过过滤不可靠传感器数据流可将预测准确率提升约10%。STARNet的代码已开源：\url{https://github.com/sinatayebati/STARNet}。