Real-time robotic systems require advanced perception, computation, and action capability. However, the main bottleneck in current autonomous systems is the trade-off between computational capability, energy efficiency and model determinism. World modeling, a key objective of many robotic systems, commonly uses occupancy grid mapping (OGM) as the first step towards building an end-to-end robotic system with perception, planning, autonomous maneuvering, and decision making capabilities. OGM divides the environment into discrete cells and assigns probability values to attributes such as occupancy and traversability. Existing methods fall into two categories: traditional methods and neural methods. Traditional methods rely on dense statistical calculations, while neural methods employ deep learning for probabilistic information processing. Recent works formulate a deterministic theory of neural computation at the intersection of cognitive science and vector symbolic architectures. In this study, we propose a Fourier-based hyperdimensional OGM system, VSA-OGM, combined with a novel application of Shannon entropy that retains the interpretability and stability of traditional methods along with the improved computational efficiency of neural methods. Our approach, validated across multiple datasets, achieves similar accuracy to covariant traditional methods while approximately reducing latency by 200x and memory by 1000x. Compared to invariant traditional methods, we see similar accuracy values while reducing latency by 3.7x. Moreover, we achieve 1.5x latency reductions compared to neural methods while eliminating the need for domain-specific model training.

翻译：实时机器人系统需要先进的感知、计算与行动能力。然而，当前自主系统的主要瓶颈在于计算能力、能效与模型确定性之间的权衡。世界建模作为许多机器人系统的核心目标，通常将占据栅格地图（OGM）作为构建具备感知、规划、自主机动与决策能力的端到端机器人系统的第一步。OGM将环境划分为离散单元，并为占据状态、可通过性等属性分配概率值。现有方法可分为两类：传统方法与神经方法。传统方法依赖于密集的统计计算，而神经方法则采用深度学习进行概率信息处理。近期研究在认知科学与向量符号架构的交叉领域提出了神经计算的确定性理论。在本研究中，我们提出了一种基于傅里叶变换的超维OGM系统VSA-OGM，结合香农熵的创新应用，在保持传统方法可解释性与稳定性的同时，兼具神经方法提升的计算效率。我们的方法在多个数据集上得到验证，达到了与传统协变方法相当的精度，同时将延迟降低约200倍、内存占用减少约1000倍。与传统不变方法相比，在精度相近的情况下实现了3.7倍的延迟降低。此外，与神经方法相比，我们在无需领域特定模型训练的前提下，实现了1.5倍的延迟降低。