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AlgoRondo animated hero banner

C++ SLAM Point Cloud Integrated Navigation LLM Curious

algo@rondo:~$ whoami
Algorithm Engineer @ RTK industry

algo@rondo:~$ bias
robustness > hype && accuracy > decoration

algo@rondo:~$ side-channel
Music

Contribution Pulse

Contribution Snake

Signal / 信号

I build algorithms that are supposed to keep working after the demo ends. My day job lives in the RTK world: SLAM, point cloud processing, integrated navigation, and a lot of C++. Lately, a growing part of my attention has shifted toward LLM systems and the engineering patterns around them.

我做的不是只在演示里好看的算法,而是要在真实环境里继续工作的算法。 我的主要工作在 RTK 相关场景里,核心方向是 SLAM点云处理组合导航,以及大量基于 C++ 的算法实现。 最近我也越来越关注 LLM 系统,以及它背后的工程方法和系统设计。

Why AlgoRondo / 为什么叫 AlgoRondo

AlgoRondo comes from two things I like: algorithms and music. A rondo returns to a theme, but never in exactly the same way twice. That feels familiar: structure, recurrence, variation, timing.

AlgoRondo 这个名字来自我喜欢的两样东西:算法音乐。 Rondo 是一种不断回到主题、但每次又带着变化的结构。 这和工程很像:结构、重复、变化、节奏。

Core Domains / 技术坐标

Domain Notes
C++ My default language for algorithm work
LiDAR SLAM Geometry-heavy systems, estimation, mapping, robustness
Point Cloud Processing, representation, filtering, and spatial reasoning
Integrated Navigation Multi-source fusion, system behavior, stability
Classical CV Traditional computer vision methods are still part of my toolbox
LLM Current curiosity and likely next growth vector

Bias / 工作偏好

  • robustness > hype

  • accuracy > decoration

  • explainability > mystery

  • systems that survive noise, drift, and messy data

  • 鲁棒性 比热闹更重要

  • 准确性 比表面效果更重要

  • 可解释性 比神秘感更重要

  • 我更相信那些能扛住噪声、漂移和脏数据的系统

Offstage / 幕后

Most of the substantial engineering work I do lives in private company repositories, so I cannot show those projects directly here. This profile is not a public dump of everything I have built. It is a compact map of what I work on, how I think, and where I may be heading next.

我很多核心工程工作都在公司的私有仓库里,因此没法直接把项目公开展示在这里。 所以这个主页不是一个“全部作品公开陈列区”。 它更像是一张压缩过的技术地图,说明我在做什么、怎么思考,以及接下来可能走向哪里。

Current Vector / 当前向量

  • Still grounded in geometry-driven systems

  • Looking more seriously at LLM applications and evaluation

  • Curious about where robotics pipelines and foundation models may eventually meet

  • Usually thinking with music on

  • 仍然扎根在以几何和估计为核心的系统里

  • 正在更认真地理解 LLM 应用与评估

  • 对机器人算法链路和基础模型未来的交汇点很感兴趣

  • 大多数难题,都是戴着耳机想出来的

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