Visual odometry (VO) is a method used to estimate self-motion of a mobile robot using visual sensors. Unlike odometry based on integrating differential measurements that can accumulate errors, such as inertial sensors or wheel encoders, VO is not compromised by drift. However, image-based VO is computationally demanding, limiting its application in use cases with low-latency, low-memory and low-energy requirements. Neuromorphic hardware offers low-power solutions to many vision and artificial intelligence problems, but designing such solutions is complicated and often has to be assembled from scratch. Here we propose the use of vector symbolic architecture (VSA) as an abstraction layer to design algorithms compatible with neuromorphic hardware. Building from a VSA model for scene analysis, described in our companion paper, we present a modular neuromorphic algorithm that achieves state-of-the-art performance on two-dimensional VO tasks. Specifically, the proposed algorithm stores and updates a working memory of the presented visual environment. Based on this working memory, a resonator network estimates the changing location and orientation of the camera. We experimentally validate the neuromorphic VSA-based approach to VO with two benchmarks: one based on an event-camera dataset and the other in a dynamic scene with a robotic task.

视觉里程计（VO）是一种使用视觉传感器估计移动机器人自我运动的方法。与基于积分差分测量的里程计（例如惯性传感器或车轮编码器）不同，里程计会累积误差，VO 不会受到漂移的影响。然而，基于图像的 VO 计算要求较高，限制了其在低延迟、低内存和低能耗需求的用例中的应用。神经形态硬件为许多视觉和人工智能问题提供了低功耗解决方案，但设计此类解决方案很复杂，并且通常必须从头开始组装。在这里，我们建议使用矢量符号架构（VSA）作为抽象层来设计与神经形态硬件兼容的算法。根据我们的配套论文中描述的用于场景分析的 VSA 模型构建，我们提出了一种模块化神经形态算法，该算法在二维 VO 任务上实现了最先进的性能。具体来说，所提出的算法存储并更新所呈现的视觉环境的工作记忆。根据该工作记忆，谐振器网络估计相机不断变化的位置和方向。我们通过两个基准实验验证基于神经拟态 VSA 的 VO 方法：一个基于事件相机数据集，另一个基于机器人任务的动态场景。