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最棒の写给机器人/自动驾驶工程师的SLAM速成课
Published:
面向不做SLAM但是要用到位姿和地图的感知、决策/规控算法工程师以及做多模态大模型（VLA？）、RL或者做产品经理的同学。本文也适合作为SLAM入门札记一则，提供直观理解。
写给工程师的僅里叶变换直觉理解
Published:
从更高层次的视角理解僅里叶变换的本质，不仅仅是教你怎么算数。从函数作为无限维向量的角度，理解为什么需要引入复数域，以及微分算子在其中的作用。
速通足式机器人运动控制locomotion（一）大纲&动力学建模
Published:
适合非控制背景的做RL locomotion的同学进行考古，也适合做优化控制的同学进军 learning based。这是一系列综述类但充满细节和直觉的文章。
SLAM速递：GLIM GPU加速的局部平滑LVI且全局一致SLAM系统
Published:
工程性非常强的一篇论文，使用VG-ICP进行点云匹配，结合滑窗平滑和基于关键帧的点云匹配，可以处理几秒钟的完全退化的lidar数据。支持多相机视觉特征约束的紧密耦合，全局轨迹优化模块最小化子地图之间的配准误差。
睿频一下ADRC自抗扰控制
Published:
深入分析ADRC自抗扰控制的本质，通过阅读韩京清研究员的经典论文”From PID to Active Disturbance Rejection Control”，剖析ADRC的核心思想和技术细节。文章从控制理论的角度客观评价ADRC的优缺点。
状态空间方程——系统模态和极点
Published:
深入讲解线性时不变系统的模态分析，通过特征值分解理解系统的动态特性。文章从离散系统和连续系统两个角度，阐述了系统矩阵特征值与系统行为的关系，以及极点与模态的本质联系。
Kalman秩判据的本质：LTIS的能控性
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从本质和直觉的角度分析Kalman秩判据，详细证明了离散和连续情形下的能控性和能达性。通过数学推导展示了能控性矩阵的物理意义，以及为什么系统的能控性可以通过秩判据来判定。
PBH判据的本质（一）离散线性系统的能控性
Published:
深入分析PBH（Popov-Belevitch-Hautus）判据的本质含义，从几何和代数的角度理解为什么控制矩阵B的列必须张成系统矩阵A每个特征值对应的零空间。文章还讨论了控制能力的度量和系统模态的可控程度。
SLAM中使用李群和李代数的直觉
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提供对SLAM中李群和李代数应用的直观理解，解释为什么在处理旋转和位姿时需要使用这些数学工具。重点讨论李群的光滑流形性质，以及李代数如何帮助我们在线性空间中处理旋转的不确定性和优化问题。
控制中拉普拉斯变换是如何推出来的？
Published:
从希尔伯特空间和线性代数的角度，直观地解释拉普拉斯变换在控制理论中的推导过程和物理意义。通过类比有限维向量空间的基变换，理解为什么要选择指数函数作为基底。
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publications
Efficient Invariant Kalman Filter for Inertial-based Odometry with Large-sample Environmental Measurements
Published in IEEE Transactions on Robotics (TRO, under review), 2024
An invariant Kalman filter design that models error distribution on the SE₂(3) manifold rather than traditional SO(3)×ℝ³ or SE(3), making the dynamics in error states become linear autonomous systems. This improves observer consistency and eliminates linearization errors. The framework demonstrates superior robustness in sensor-degraded scenarios such as long corridors and white walls.
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Consistent and Optimal Solution to Camera Motion Estimation
Published in IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024
A novel approach for camera motion estimation that achieves asymptotic Gaussianity in noise distribution through special error construction for essential matrix estimation. The method enables bias elimination through variance estimation and achieves maximum likelihood optimal estimation via single-step Gauss-Newton iteration, reaching the Cramér-Rao lower bound.
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Distributed Invariant Kalman Filter for Object-level Multi-robot Pose SLAM
Published in IEEE International Conference on Robotics and Automation (ICRA 2025), 2025
A distributed left-invariant Kalman filter approach that utilizes semantic object-level 6-DoF estimations from Pose-CNN outputs and relative pose measurements between robots as filter observations. The method addresses uncertainty estimation issues caused by unknown correlations in inter-robot pose estimation through covariance intersection, ensuring system robustness when partial robots experience observation degradation.
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SCORE: Saturated Consensus Relocalization in Semantic Line Maps
Published in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2025), 2025
A robust visual relocalization framework that addresses the challenges of large outlier ratios and excessive map storage requirements using semantic line representations. The method employs Perspective-N-Lines with a saturated consensus approach and accelerated branch-and-bound algorithm for two-stage rotation and translation estimation, achieving robust performance even with 99% outlier matches.
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talks
Talk 1 on Relevant Topic in Your Field
Published:
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Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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