优化点云配准算法内存使用，实现在嵌入式GPU上高效运行。
A dynamic memory assignment strategy for dilation-based ICP algorithm on embedded GPUs
- 提出动态内存分配策略，针对膨胀机制优化显存占用。
- 内存消耗降低97%以上，性能与原版基本一致。
- 适合资源受限的嵌入式系统部署，如移动机器人、无人机。
本文提出一种面向嵌入式GPU的内存高效优化策略，用于高性能点云配准算法VANICP，使其能在硬件资源受限的设备上轻量级运行。VANICP是一种近期提出的加速框架，通过基于膨胀的信息传播机制将全局最近邻搜索转为局部化过程，显著降低近似最近邻搜索（NNS）的计算复杂度。然而，其原始实现需大量内存，限制了在嵌入式系统中的部署。为此，本文设计了一种面向GPU的动态内存分配策略，优化膨胀操作的内存使用。基于该策略，构建了增强版VANICP框架，在保持原有性能的同时，内存消耗降低超过97%。源代码已公开于：https://github.com/changqiong/VANICP4Em.git。
原文摘要 · Abstract (English)
This paper proposes a memory-efficient optimization strategy for the high-performance point cloud registration algorithm VANICP, enabling lightweight execution on embedded GPUs with constrained hardware resources. VANICP is a recently published acceleration framework that significantly improves the computational efficiency of point-cloud-based applications. By transforming the global nearest neighbor search into a localized process through a dilation-based information propagation mechanism, VANICP greatly reduces the computational complexity of the NNS. However, its original implementation demands a considerable amount of memory, which restricts its deployment in resource-constrained environments such as embedded systems. To address this issue, we propose a GPU-oriented dynamic memory assignment strategy that optimizes the memory usage of the dilation operation. Furthermore, based on this strategy, we construct an enhanced version of the VANICP framework that achieves over 97% reduction in memory consumption while preserving the original performance. Source code is published on: https://github.com/changqiong/VANICP4Em.git.
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