Author name cluster

Lei Qin

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7 papers

2 author rows

IROS Conference 2023 Conference Paper

SMART-Rain: A Degradation Evaluation Dataset for Autonomous Driving in Rain

Chen Zhang 0018
Zefan Huang
Hongliang Guo 0003
Lei Qin
Marcelo H. Ang
Daniela Rus

Autonomous driving in the rain remains a challenge. One main problem is performance degradation caused by rain. This work introduces a new dataset to study this problem. Our dataset is collected from a full-scale vehicle equipped with a 3D LiDAR sensor and multiple forward-facing cameras under various rainy conditions. In addition, rainfall intensity is recorded in real-time from a rain sensor. The combination of sensor and rainfall intensity measurement is designed for studying algorithm performance under different levels of rainfall. In this work, in addition to presenting dataset creation details, we also introduce three degradation evaluation tasks with baseline results, including rainfall intensity estimation, LiDAR degradation estimation, and 2D object detection evaluation. This dataset, development kit, and baseline codes will be made available at https://smart-rain-dataset.github.io/

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ICRA Conference 2021 Conference Paper

Deep Imitation Learning for Autonomous Navigation in Dynamic Pedestrian Environments

Lei Qin
Zefan Huang
Chen Zhang 0018
Hongliang Guo 0003
Marcelo H. Ang
Daniela Rus

Navigation through dynamic pedestrian environments in a socially compliant manner is still a challenging task for autonomous vehicles. Classical methods usually lead to unnatural vehicle behaviours for pedestrian navigation due to the difficulty in modeling social conventions mathematically. This paper presents an end-to-end path planning system that achieves autonomous navigation in dynamic environments through imitation learning. The proposed system is based on a fully convolutional neural network that maps the raw sensory data into a confidence map for path extraction. Additionally, a classification network is introduced to reduce the unnecessary re-plannings and ensures that the vehicle goes back to the global path when re-planning is not needed. The imitation learning based path planner is implemented on an autonomous wheelchair and tested in a new real-world dynamic pedestrian environment. Experimental results show that the proposed system is able to generate paths for different driving tasks, such as pedestrian following, static and dynamic obstacles avoidance, etc. In comparison to the state-of-the-art method, our system is superior in terms of generating human-like trajectories.

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IROS Conference 2021 Conference Paper

Group Multi-Object Tracking for Dynamic Risk Map and Safe Path Planning

Lyuyu Shen
Hongliang Guo 0003
Yechao Bai
Lei Qin
Marcelo H. Ang
Daniela Rus

This paper studies the group multi-object tracking (MOT) problem in dynamic pedestrian environments, with intended application to safe navigation for autonomous vehicles. We complete a full autonomous vehicle navigation pipeline from object detection, tracking, grouping, to risk map generation and safe path planning. Our main contribution is to instantiate a group multi-object tracking algorithm, which provides the crucial grouped activity information, i. e. group position, group velocity, group size, to the risk map generator, and therewith produce a stable and robust risk map for the downstream safe path planner. Experimental results with real world data show the socially acceptable, robust and stable performance of the proposed algorithm over its individual MOT counterpart.

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IROS Conference 2017 Conference Paper

A frog-inspired swimming robot based on dielectric elastomer actuators

Yucheng Tang
Lei Qin
Xiaoning Li
Chee-Meng Chew
Jian Zhu 0005

Frogs are capable of multiple locomotion modes including jumping and swimming, which enables them to adapt to various environmental conditions. This paper demonstrates a frog-inspired robot, which can mimic the swimming motion of a natural frog. The robot is developed based on dielectric elastomer actuators, which exhibits muscle-like behavior such as large voltage-induced deformation, high energy density, fast response and low weight. Inspired by the webbed feet of a frog, the foot actuator of the swimming robot is able to increase its projected area by 66% when subject to high voltage. Actuation of the foot actuators can significantly improve the averaged peak thrust by 34. 5%. The total mass of the two dielectric elastomer actuators is 14g which only accounts for 13% of its total mass of 108g. The measured average swimming speed for a square wave voltage of 5kV and 0. 25Hz is 19mm/s for the swimming robot. Future work of the project includes optimal design and control of this soft robot.

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AAAI Conference 2017 Conference Paper

Cross-View People Tracking by Scene-Centered Spatio-Temporal Parsing

Yuanlu Xu
Xiaobai Liu
Lei Qin
Song-Chun Zhu

In this paper, we propose a Spatio-temporal Attributed Parse Graph (ST-APG) to integrate semantic attributes with trajectories for cross-view people tracking. Given videos from multiple cameras with overlapping ﬁeld of view (FOV), our goal is to parse the videos and organize the trajectories of all targets into a scene-centered representation. We leverage rich semantic attributes of human, e. g. , facing directions, postures and actions, to enhance cross-view tracklet associations, besides frequently used appearance and geometry features in the literature. In particular, the facing direction of a human in 3D, once detected, often coincides with his/her moving direction or trajectory. Similarly, the actions of humans, once recognized, provide strong cues for distinguishing one subject from the others. The inference is solved by iteratively grouping tracklets with cluster sampling and estimating people semantic attributes by dynamic programming. In experiments, we validate our method on one public dataset and create another new dataset that records people’s daily life in public, e. g. , food court, ofﬁce reception and plaza, each of which includes 3-4 cameras. We evaluate the proposed method on these challenging videos and achieve promising multi-view tracking results.

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ICRA Conference 2013 Conference Paper

Closed-loop commutation control of an MRI-powered robot actuator

Christos Bergeles
Panagiotis Vartholomeos
Lei Qin
Pierre E. Dupont

Actuators that are powered, imaged and controlled by Magnetic Resonance (MR) scanners offer the potential of inexpensively providing wireless control of MR-guided robots. Similar to traditional electric motors, the MR scanner acts as the stator and generates propulsive torques on an actuator rotor containing one or more ferrous particles. To generate maximum motor torque while avoiding instabilities and slippage, closed-loop control of the electromagnetic field gradients, i. e. , commutation, is required. This paper proposes and demonstrates a method for commutation based on interleaving pulse sequences for rotor tracking and rotor propulsion. Fast rotor tracking is achieved by a new technique utilizing radio-frequency (RF) selective excitation of a properly located fiducial marker by the ferrous particle of the rotor. Optimal marker location is derived and demonstrated to provide accurate estimates of rotor angle. In addition, closed-loop commutation control is shown to increase motor torque and also to enable regulation of rotor angle.

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IROS Conference 2011 Conference Paper

MRI-powered actuators for robotic interventions

Panagiotis Vartholomeos
Lei Qin
Pierre E. Dupont

This paper presents a novel actuation technology for robotically assisted MRI-guided interventional procedures. Compact and wireless, the actuators are both powered and controlled by the MRI scanner. The design concept and performance limits are described and derived analytically. Simulation and experiments in a clinical MR scanner are used to validate the analysis and to demonstrate the capability of the approach for needle biopsies. The concepts of actuator locking mechanisms and multi-axis control are also introduced.

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