Author name cluster

Bin Lan

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

IROS Conference 2025 Conference Paper

KD-RIEKF: Kinodynamic Right-Invariant EKF for Legged Robot State Estimation

Qi Yang
Bin Lan
Bingjie Chen
Jingjing Wang
Yi Cheng
Yizhe Li
Houde Liu
Bin Liang

We present KD-RIEKF, a novel state estimation framework that incorporates kinodynamic constraints into the Right-Invariant Extended Kalman Filter (RIEKF). Our framework integrates generalized momentum-based contact estimation, centroidal dynamics, and a noise-adaptive module, improving state estimation accuracy by probabilistically adjusting propagation noise to account for contact uncertainty and sensor noise. A key innovation is the expansion of the ground reaction force (GRF) into a state variable. By using GRF-based acceleration as a measurement, our method significantly reduces estimation errors in position, velocity, and orientation. The integration of contact-force-driven adaptive noise effectively boosts the stability of estimation, especially when the system is undergoing turning, acceleration, or deceleration processes. We validated our algorithm in simulation on highly uneven terrain, showing significant enhancements in z-axis position estimation compared to RIEKF. Further experiments on the Unitree Go2 robot across different speeds demonstrated that even in high-speed scenarios over 200 meters, our method reduced position estimation relative error (RE) by 47% and orientation estimation by 42%, confirming its robustness and accuracy under dynamic locomotion.

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ICRA Conference 2023 Conference Paper

Dynamic Control Barrier Function-based Model Predictive Control to Safety-Critical Obstacle-Avoidance of Mobile Robot

Zhuozhu Jian
Zihong Yan
Xuanang Lei
Zihong Lu
Bin Lan
Xueqian Wang 0001
Bin Liang 0001

This paper presents an efficient and safe method to avoid static and dynamic obstacles based on LiDAR. First, point cloud is used to generate a real-time local grid map for obstacle detection. Then, obstacles are clustered by DBSCAN algorithm and enclosed with minimum bounding ellipses (MBEs). In addition, data association is conducted to match each MBE with the obstacle in the current frame. Considering MBE as an observation, Kalman filter (KF) is used to estimate and predict the motion state of the obstacle. In this way, the trajectory of each obstacle in the forward time domain can be parameterized as a set of ellipses. Due to the uncertainty of the MBE, the semi-major and semi-minor axes of the parameterized ellipse are extended to ensure safety. We extend the traditional Control Barrier Function (CBF) and propose Dynamic Control Barrier Function (D-CBF). We combine D-CBF with Model Predictive Control (MPC) to implement safety-critical dynamic obstacle avoidance. Experiments in simulated and real scenarios are conducted to verify the effectiveness of our algorithm. The source code is released for the reference of the community 1 1 Code: https://github.com/jianzhuozhuTHU/MPC-D-CBF. .

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IROS Conference 2022 Conference Paper

PUTN: A Plane-fitting based Uneven Terrain Navigation Framework

Zhuozhu Jian
Zihong Lu
Xiao Zhou
Bin Lan
Anxing Xiao
Xueqian Wang 0001
Bin Liang 0001

Autonomous navigation of ground robots has been widely used in indoor structured 2D environments, but there are still many challenges in outdoor 3D unstructured environments, especially in rough, uneven terrains. This paper proposed a plane-fitting based uneven terrain navigation framework (PUTN) to solve this problem. The implementation of PUTN is divided into three steps. First, based on Rapidly-exploring Random Trees (RRT), an improved sample-based algorithm called Plane Fitting RRT*(PF- RRT*) is proposed to obtain a sparse trajectory. Each sampling point corresponds to a custom traversability index and a fitted plane on the point cloud. These planes are connected in series to form a traversable “strip”. Second, Gaussian Process Regression is used to generate traversability of the dense trajectory interpolated from the sparse trajectory, and the sampling tree is used as the training set. Finally, local planning is performed using nonlinear model predictive control (NMPC). By adding the traversability index and uncertainty to the cost function, and adding obstacles generated by the real-time point cloud to the constraint function, a safe motion planning algorithm with smooth speed and strong robustness is available. Experiments in real scenarios are conducted to verify the effectiveness of the method. The source code is released for the reference of the community 1 1 Source code: https://github.com/jianzhuozhuTHU/putn. .

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