Huan Yin

Huan Yin
The Hong Kong University of Science and Technology | UST · Department of Electronic and Computer Engineering

PhD

About

41
Publications
5,593
Reads
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386
Citations
Citations since 2016
40 Research Items
380 Citations
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2016201720182019202020212022020406080100120
2016201720182019202020212022020406080100120
2016201720182019202020212022020406080100120

Publications

Publications (41)
Article
Conventional sensor-based localization relies on high-precision maps, which are generally built using specialized mapping techniques involving high labor and computational costs. In the architectural, engineering and construction industry, Building Information Models (BIM) are available and can provide informative descriptions of environments. This...
Article
Trajectory planning is a critical component in autonomous vehicles directly responsible for driving safety and efficiency during deployment. The ability to find the optimal trajectory in real-time is critical for autonomous driving. This paper presents a novel general framework using the Fast Iterative Search and Sampling (FISS) strategy for sampli...
Article
Full-text available
Lidar and visual data are affected heavily in adverse weather conditions due to sensing mechanisms, which bring potential safety hazards for vehicle navigation. Radar sensing is desirable to build a more robust navigation system. In this paper, a cross‐modality radar localisation on prior lidar maps is presented. Specifically, the proposed workflow...
Article
Economic dispatch of electricity-heat microgrid is critical for real-time power generation and storage. However, conventional economic dispatch algorithms are generally integrated with static unit models without considering dynamics of units, thus leading to difficulties for real deployment in stochastical environments. In this paper, we propose a...
Preprint
Full-text available
Conventional sensor-based localization relies on high-precision maps. These maps are generally built using specialized mapping techniques, which involve high labor and computational costs. While in the architectural, engineering and construction industry, building information models (BIMs) are available and can provide informative descriptions of e...
Preprint
LiDAR-based global localization is a fundamental problem for mobile robots. It consists of two stages, place recognition and pose estimation, and yields the current orientation and translation, using only the current scan as query and a database of map scans. Inspired by the definition of a recognized place, we consider that a good global localizat...
Preprint
Performing closed-loop grasping at close proximity to an object requires a large field of view. However, such images will inevitably bring large amounts of unnecessary background information, especially when the camera is far away from the target object at the initial stage, resulting in performance degradation of the grasping network. To address t...
Preprint
Intra-day economic dispatch of an integrated microgrid is a fundamental requirement to integrate distributed generators. The dynamic energy flows in cogeneration units present challenges to the energy management of the microgrid. In this paper, a novel approximate dynamic programming (ADP) approach is proposed to solve this problem based on value f...
Preprint
Full-text available
We present a heterogeneous localization framework for solving radar global localization and pose tracking on pre-built lidar maps. To bridge the gap of sensing modalities, deep neural networks are constructed to create shared embedding space for radar scans and lidar maps. Herein learned feature embeddings are supportive for similarity measurement,...
Article
Full-text available
Place recognition is critical for both offline mapping and online localization. However, current single-sensor based place recognition still remains challenging in adverse conditions. In this paper, a heterogeneous measurement based framework is proposed for long-term place recognition, which retrieves the query radar scans from the existing lidar...
Preprint
Full-text available
In-flight objects capture is extremely challenging. The robot is required to complete trajectory prediction, interception position calculation and motion planning in sequence within tens of milliseconds. As in-flight uneven objects are affected by various kinds of forces, motion prediction is difficult for a time-varying acceleration. In order to c...
Article
Compared to the onboard camera and laser scanner, radar sensor provides lighting and weather invariant sensing, which is naturally suitable for long-term localization under adverse conditions. However, radar data is sparse and noisy, resulting in challenges for radar mapping. On the other hand, the most popular available map currently is built by l...
Article
Global localization and kidnapping are two challenging problems in robot localization. The popular method, Monte Carlo Localization (MCL) addresses the problem by iteratively updating a set of particles with a “sampling-weighting” loop. Sampling is decisive to the performance of MCL [1] . However, traditional MCL can only sample from a uniform di...
Article
Global localization is essential for robot navigation, of which the first step is to retrieve a query from the map database. This problem is called place recognition. In recent years, LiDAR scan based place recognition has drawn attention as it is robust against the appearance change. In this letter, we propose a LiDAR-based place recognition metho...
Preprint
Full-text available
Place recognition is critical for both offline mapping and online localization. However, current single-sensor based place recognition still remains challenging in adverse conditions. In this paper, a heterogeneous measurements based framework is proposed for long-term place recognition, which retrieves the query radar scans from the existing lidar...
Preprint
Full-text available
Global localization is essential for robot navigation, of which the first step is to retrieve a query from the map database. This problem is called place recognition. In recent years, LiDAR scan based place recognition has drawn attention as it is robust against the environmental change. In this paper, we propose a LiDAR-based place recognition met...
Preprint
Full-text available
Radar sensor provides lighting and weather invariant sensing, which is naturally suitable for long-term localization in outdoor scenes. On the other hand, the most popular available map currently is built by lidar. In this paper, we propose a deep neural network for end-to-end learning of radar localization on lidar map to bridge the gap. We first...
Preprint
Full-text available
Global localization and kidnapping are two challenging problems in robot localization. The popular method, Monte Carlo Localization (MCL) addresses the problem by sampling uniformly over the state space, which is unfortunately inefficient when the environment is large. To better deal with the the problems, we present a proposal model, named Deep Mu...
Preprint
Full-text available
Radar and lidar, provided by two different range sensors, each has pros and cons of various perception tasks on mobile robots or autonomous driving. In this paper, a Monte Carlo system is used to localize the robot with a rotating radar sensor on 2D lidar maps. We first train a conditional generative adversarial network to transfer raw radar data t...
Article
Large scale 3D maps constructed via LiDAR sensor are widely used on intelligent vehicles for localization in outdoor scenes. However, loading, communication and processing of the original dense maps are time consuming for onboard computing platform, which calls for a more concise representation of maps to reduce the complexity but keep the performa...
Article
Autonomous mobile vehicles are expected to perform persistent and accurate localization with low-cost equipment. To achieve this goal, we propose a stereo camera based visual localization method using a modified laser map, which takes the advantage of both the low cost of camera, and high geometric precision of laser data to achieve long-term perfo...
Article
Global localization in 3D point clouds is a challenging task for mobile vehicles in outdoor scenarios, which requires the vehicle to localize itself correctly in a given map without prior knowledge of its pose. This is a critical component of autonomous vehicles or robots on the road for handling localization failures. In this paper, based on reduc...
Preprint
Visual localization is one of the primary capabilities for mobile robots. Long-term visual localization in real time is particularly challenging, in which the robot is required to efficiently localize itself using visual data where appearance may change significantly over time. In this paper, we propose a cloud-based visual localization system targ...
Article
Full-text available
Long term mapping and localization are the primary components for mobile robots in real world application deployment, of which the crucial challenge is the robustness and stability. In this paper, we introduce a topological local-metric framework (TLF), aiming at dealing with environmental changes, erroneous measurements and achieving constant comp...
Preprint
Full-text available
Map construction in large scale outdoor environment is of importance for robots to robustly fulfill their tasks. Massive sessions of data should be merged to distinguish low dynamics in the map, which otherwise might debase the performance of localization and navigation algorithms. In this paper we propose a method for multi-session map constructio...
Article
Full-text available
Long-term visual localization in outdoor environment is a challenging problem, especially faced with the cross-seasonal, bi-directional tasks and changing environment. In this paper we propose a novel visual inertial localization framework that localizes against the LiDAR-built map. Based on the geometry information of the laser map, a hybrid bundl...
Preprint
Full-text available
Global localization in 3D point clouds is a challenging problem of estimating the pose of robots without priori knowledge. In this paper, a solution to this problem is presented by achieving place recognition and metric pose estimation in the global priori map. Specifically, we present a semi-handcrafted representation learning method for LIDAR poi...

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