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Han Wang 0001

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11 papers

1 author row

ICRA Conference 2024 Conference Paper

MMAUD: A Comprehensive Multi-Modal Anti-UAV Dataset for Modern Miniature Drone Threats

Shenghai Yuan 0001
Yizhuo Yang 0001
Thien Hoang Nguyen
Thien-Minh Nguyen
Jianfei Yang
Fen Liu
Jianping Li 0004
Han Wang 0001

In response to the evolving challenges posed by small unmanned aerial vehicles (UAVs), which possess the potential to transport harmful payloads or independently cause damage, we introduce MMAUD: a comprehensive Multi-Modal Anti-UAV Dataset. MMAUD addresses a critical gap in contemporary threat detection methodologies by focusing on drone detection, UAV-type classification, and trajectory estimation. MMAUD stands out by combining diverse sensory inputs, including stereo vision, various Lidars, Radars, and audio arrays. It offers a unique overhead aerial detection vital for addressing real-world scenarios with higher fidelity than datasets captured on specific vantage points using thermal and RGB. Additionally, MMAUD provides accurate Leica-generated ground truth data, enhancing credibility and enabling confident refinement of algorithms and models, which has never been seen in other datasets. Most existing works do not disclose their datasets, making MMAUD an invaluable resource for developing accurate and efficient solutions. Our proposed modalities are cost-effective and highly adaptable, allowing users to experiment and implement new UAV threat detection tools. Our dataset closely simulates real-world scenarios by incorporating ambient heavy machinery sounds. This approach enhances the dataset’s applicability, capturing the exact challenges faced during proximate vehicular operations. It is expected that MMAUD can play a pivotal role in advancing UAV threat detection, classification, trajectory estimation capabilities, and beyond. Our dataset, codes, and designs will be available in https://ntu-aris.github.io/MMAUD.

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IROS Conference 2021 Conference Paper

Self-critical Learning of Influencing Factors for Trajectory Prediction using Gated Graph Convolutional Network

Niraj Bhujel
Wei-Yun Yau
Han Wang 0001
Vijay Prakash Dwivedi

Forecasting future trajectories of multiple pedestrians in a crowded environment is a challenging problem due to the complex interactions among the pedestrians. The interactions can be asymmetric and their influences may vary over time. Moreover, each pedestrian can exhibit different behavior at any given time and context and thus they may have multiple future possible trajectories. In this work, we present a Gated Graph Convolutional Network (GatedGCN) based trajectory prediction model that explicitly deal with the asymmetric influences among the adjacent pedestrians through edge-wise gating mechanism. Through GatedGCN only, an overall average improvement of 16% and 18% was achieved on the two performance metrics over the state-of-the-art trajectory forecasting methods. Next, we tackle the problem of learning multi-modal distributions of each pedestrian trajectory using variational auto-encoders (VAEs). However, trajectories sampled from the learned distribution usually ignore the factors affecting the pedestrian motion such as collision avoidance and the target destination. While many of the existing approaches focus on learning such factors during the trajectory encoding process, we proposed a novel self-critical learning approach based on Actor-Critic framework to learn such factors during the trajectory generation process. We empirically show that our method creates fewer number of collisions than the existing methods on popular trajectory forecasting benchmarks.

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ICRA Conference 2020 Conference Paper

Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection

Han Wang 0001
Chen Wang 0033
Lihua Xie 0001

Loop closure detection is an essential and challenging problem in simultaneous localization and mapping (SLAM). It is often tackled with light detection and ranging (LiDAR) sensor due to its view-point and illumination invariant properties. Existing works on 3D loop closure detection often leverage on matching of local or global geometrical-only descriptors which discard intensity reading. In this paper we explore the intensity property from LiDAR scan and show that it can be effective for place recognition. We propose a novel global descriptor, intensity scan context (ISC), that explores both geometry and intensity characteristics. To improve the efficiency for loop closure detection, an efficient two-stage hierarchical re-identification process is proposed, including binary-operation based fast geometric relation retrieval and intensity structure re-identification. Thorough experiments including both local experiment and public datasets test have been conducted to evaluate the performance of the proposed method. Our method achieves better recall rate and recall precision than existing geometric-only methods.

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IROS Conference 2020 Conference Paper

Online Visual Place Recognition via Saliency Re-identification

Han Wang 0001
Chen Wang 0033
Lihua Xie 0001

As an essential component of visual simultaneous localization and mapping (SLAM), place recognition is crucial for robot navigation and autonomous driving. Existing methods often formulate visual place recognition as feature matching, which is computationally expensive for many robotic applications with limited computing power, e. g. , autonomous driving and cleaning robot. Inspired by the fact that human beings always recognize a place by remembering salient regions or landmarks that are more attractive or interesting than others, we formulate visual place recognition as saliency reidentification. In the meanwhile, we propose to perform both saliency detection and re-identification in frequency domain, in which all operations become element-wise. The experiments show that our proposed method achieves competitive accuracy and much higher speed than the state-of-the-art feature-based methods. The proposed method is open-sourced and available at https://github.com/wh200720041/SRLCD.git.

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IROS Conference 2020 Conference Paper

Towards Understanding and Inferring the Crowd: Guided Second Order Attention Networks and Re-identification for Multi-object Tracking

Niraj Bhujel
Jun Li 0005
Wei-Yun Yau
Han Wang 0001

Multi-human tracking in the crowded environment is a challenging problem due to occlusions, pose change, viewpoint variation and cluttered background. In this work, we propose a robust feature learning for tracking-by-detection methods based on second-order attention network that can capture higher-order relationships between salient features at the early stages of Convolutional Neural Network (CNN). Guided Second-Order Attention Network (GSAN) that, unlike the existing attention learning methods which are weakly-supervised, uses a supervisory signal based on the quality of the self-learned attention maps. More specifically, GSAN looks into the attended maps of a person having the highest confidence and supervise itself to look into the correct regions in the images of the person. Attention maps learned this way are spatially aligned and thus robust to camera-view changes and body pose variations. We verify the effectiveness of our approach by comparing with the state-of-the-art methods on challenging person re-identification and multi object tracking (MOT) datasets.

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IROS Conference 2012 Conference Paper

A novel torchlight data association strategy for surface registration

Han Wang 0001
Ying Ying

This paper presents a novel method for rigid surface registration using torchlight structure as data association, and the new method improves the correctness of point matching. When two sets of point clouds are merged, assume a set of torchlight beams parallely pass through them, and each light ray passes the overlapped data twice, one on each set. The Euclidean distance on such pair is taken as measurement of the separation. When the two sets are optimally aligned, the registration error is minimized. Hence, surface registration problem is reduced to a six degree of freedom searching procedure. Preprocessing, optimization, and acceleration modules are introduced to normalize raw data, explore registration space, and reduce execution time. Unlike the Iterative Closest Point (ICP) algorithm, the proposed approach does not require pre-alignment information. Secondly, the performance of ICP is poor when the overlapped area between two sets is not sufficiently large. The proposed approach does not suffer from these problems. Based on various experiments, the proposed approach shows the superior performance over ICP.

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ICRA Conference 2006 Conference Paper

Large-scale Loop-closing with Pictorial Matching

Cheng Chen
Han Wang 0001

This paper presents a mapping method that can accurately map large environment with one single robot by visiting the environment for only once, and the resulting map can provide thorough 3D description for the environment in a predefined global coordinate. Our first contribution is to represent the map as a collection of submaps arranged in a deformable configuration, and to perform loop-closing by registering this submap configuration to an aerial image. The second contribution is to introduce the active contour technique to the SLAM domain, so that the registration is efficiently solved in an iterative energy minimization process. The constraints from robot mapping are modeled as forces trying to keep the submaps consistent to each other, while the pictorial matching is represented by forces guiding submaps to a globally consistent configuration. In the experiment, we demonstrate the proposed algorithm's capability to close a 1, 890 meters with only one visiting. The result is compared with ground truth, and high accuracy is observed

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IROS Conference 2005 Conference Paper

Appearance-based topological Bayesian inference for loop-closing detection in cross-country environment

Cheng Chen
Han Wang 0001

In this paper, an appearance-based environment modelling technique is presented. Based on this approach, the probabilistic Bayesian inference can work together with symbolic topological map to re-localize a mobile robot. One prominent advantage offered by this algorithm is that, it can be applied to cross-country environment where no features or landmarks are available. Furthermore, the loop-closing can be detected independent of estimated map and vehicle location. High dimensional laser measurements are projected into a low dimensional space (mapspace) which describes the appearance of the environment. Since laser scans from the same region share the similar appearance, after the projection, they are expected to form a distinct cluster in the low dimensional space. This small cluster essentially encodes the appearance information of the specific region in the environment, and it can be approximated by a Gaussian distribution. This Gaussian model can serve as the 'joint' between the topological map structure and the probabilistic Bayesian inference. By employing such 'joints', the Bayesian inference in the metric level can be conveniently implemented on topological level. Based on appearance, the proposed inference process is thus completely independent of local metric features. Extensive experiments were conducted using a tracked vehicle travelling in an open jungle environments. Results from live runs verified the feasibility of the proposed methods to detect loop-closing. The performances are also given and thoroughly analyzed.

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ICRA Conference 2004 Conference Paper

Vehicle Following with Obstacle Avoidance Capabilities in Natural Environments

Teck Chew Ng
Javier Ibañez-Guzmán
Jian Shen
Zhiming Gong
Han Wang 0001
Cheng Chen

A robust vehicle following system with obstacle avoidance capabilities for operation in natural environments is described in this paper. By combining a novel vehicle-tracking and detection algorithm with our path-planner for autonomous navigation, it was possible for a tracked logistics armoured ambulance carrier to follow a multi purpose vehicle in an equatorial jungle where few non-paved roads and markers exist. With this new approach, vehicle following performance is enhanced and vehicle safety ensured. Field trials performed in tropical jungle conditions have demonstrated the validity of the approach; results from the field works are included and discussed in this paper.

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IROS Conference 2001 Conference Paper

Embedding cooperation in robots to play soccer game

Hui Wang
Han Wang 0001
Chunmiao Wang
William Y. C. Soh

Robotic soccer provides an opportunity to explore such a challenging research topic that multiple agents (physical robots or sofbots) work together in a realtime, noisy and adversarial environment to obtain specific objectives. It requires each agent can not only deal with infinite unpredictable situations, but also present cooperation with others. The previous researches about cooperation often put emphasis on task decomposition and conflict avoidance among team members. In this paper, we describe a robot architecture, which addresses "scaling cooperation" among robots, and meanwhile keeps each robot making decision independently. The architecture is based on "ideal cooperation" principle and implemented for Small Robot League in RoboCup Experimental results prove its effectiveness and reveal several primary characteristics of behaviors in robotic soccer. Finally, some important problems of future work are discussed.

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IROS Conference 1999 Conference Paper

Fuzzy logic Kalman filter estimation for 2-wheel steerable vehicles

Han Wang 0001
Ching Tard Goh

This article addresses the multisensor data fusion problem in the position estimation of a two-wheel steerable vehicle converted from a golf buggy. The fusion is based mainly on the extended Kalman filter approach. This paper describes how the estimator integrates sensory data from the differential global positioning system (DGPS), gyroscope and odometry to provide recursively an optimal estimate of the position and orientation of the vehicle. In addition, a modified kinematic process model is proposed that accounts and estimates the side-slip angles at the wheels. A technique that incorporates fuzzy logic to maintain the estimation consistency of the filter is also described Finally, the filter's performance is evaluated with simulations conducted using true data obtained from field trials.

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