Visual Odometry (VO) is a method 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, visual odometry is not compromised by drift. However, image-based VO is computationally demanding, limiting its application in use cases with low-latency, -memory, and -energy requirements. Neuromorphic hardware offers low-power solutions to many vision and AI problems, but designing such solutions is complicated and often has to be assembled from scratch. Here we propose to use 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.

翻译：视觉里程计（VO）是一种利用视觉传感器估计移动机器人自身运动的方法。与基于积分差分测量（如惯性传感器或轮式编码器）且可能累积误差的里程计不同，视觉里程计不受漂移问题的影响。然而，基于图像的视觉里程计计算需求高，这限制了其在低延迟、低内存和低能耗应用场景中的使用。神经形态硬件为许多视觉与人工智能问题提供了低功耗解决方案，但此类方案的设计较为复杂，且通常需要从头构建。本文提出使用向量符号架构（VSA）作为抽象层来设计与神经形态硬件兼容的算法。基于我们另一篇配套论文中描述的VSA场景分析模型，我们提出了一种模块化的神经形态算法，该算法在二维视觉里程计任务中达到了最先进的性能。具体而言，所提算法存储并更新所呈现视觉环境的工作记忆。基于该工作记忆，一个谐振网络估计相机位置与方向的变化。我们通过两个基准测试对基于神经形态VSA的视觉里程计方法进行了实验验证：一个基于事件相机数据集，另一个则是在动态场景中结合机器人任务进行测试。