用于物体位姿估计的神经形态处理器上的鲁棒PnP

中文摘要 AI 辅助

神经形态计算因其更高的能源效率在机器人感知中受到关注。虽然基于神经网络的方法能更轻松利用神经形态计算机的分布式和并行结构，但为非学习任务设计神经形态解决方案却不那么直接。这阻碍了神经形态计算在依赖学习和非学习组件的感知管道中的应用，如物体位姿估计（OPE）。本文提出一种用于鲁棒PnP的新型神经形态可部署公式，给出易有异常值的2D-3D对应关系时，确定具有最多内点的物体位姿。该方法基于一种可在神经形态处理器上执行的刚体位姿鲁棒最小二乘估计分布式算法。还设计了一个脉冲神经网络（SNN）从事件数据预测2D地标。结果表明神经形态鲁棒PnP在神经形态硬件（Intel Loihi 2）上具有更高能源效率且精度有竞争力。

英文摘要

Neuromorphic computing is gaining attention in robotic perception due to its higher energy efficiency. While neural network-based methods can more readily exploit the distributed and parallelized structure of neuromorphic computers, crafting neuromorphic solutions for non-learning tasks is less straightforward. This hampers the usage of neuromorphic computing for perception pipelines that depend on both learning and non-learning components, such as object pose estimation (OPE) where state-of-the-art methods use a deep network to predict 2D landmarks and nonlinear optimization to solve perspective-n-point (PnP). In this paper, we propose a novel neuromorphic-deployable formulation for robust PnP, where given outlier-prone 2D-3D correspondences, the object pose with the largest number of inliers is determined. Underpinning our method is a distributed algorithm for robust least squares estimation of rigid body pose that can be executed on a neuromorphic processor. We also design a spiking neural network (SNN) to predict 2D landmarks from event data, where the main layers of the SNN were designed according to the principles of spiking neurons. Overall, our work enables neuromorphic treatment of the major stages of an OPE pipeline, from event sensing and learned landmark prediction, to geometric optimization for robust PnP. Results on neuromophic hardware (Intel Loihi 2) indicate the higher energy efficiency our neuromorphic robust PnP, while achieving competitive accuracy.

发表机构

• AI for Space Group, Adelaide University（太空人工智能团队，阿德莱德大学）

机构由 AI 辅助整理，请以论文原文为准。

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12 pages