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Hongbin Ma

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5 papers

2 author rows

AAAI Conference 2025 Conference Paper

Cross-PCR: A Robust Cross-Source Point Cloud Registration Framework

Guiyu Zhao
Zhentao Guo
Zewen Du
Hongbin Ma

Due to the density inconsistency and distribution difference between cross-source point clouds, previous methods fail in cross-source point cloud registration. We propose a density-robust feature extraction and matching scheme to achieve robust and accurate cross-source registration. To address the density inconsistency between cross-source data, we introduce a density-robust encoder for extracting density-robust features. To tackle the issue of challenging feature matching and few correct correspondences, we adopt a loose-to-strict matching pipeline with a ``loose generation, strict selection'' idea. Under it, we employ a one-to-many strategy to loosely generate initial correspondences. Subsequently, high-quality correspondences are strictly selected to achieve robust registration through sparse matching and dense matching. On the challenging Kinect-LiDAR scene in the cross-source 3DCSR dataset, our method improves feature matching recall by 63.5 percentage points (pp) and registration recall by 57.6 pp. It also achieves the best performance on 3DMatch, while maintaining robustness under diverse downsampling densities.

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EAAI Journal 2024 Journal Article

Reference-based super-resolution reconstruction of remote sensing images based on a coarse-to-fine feature matching transformer

Chen Wang
Fuzhen Zhu
Bing Zhu
Qi Zhang
Hongbin Ma

Remote sensing image super-resolution reconstruction technology mines the deep details of remote sensing image, which has been widely used in the fields of intelligent and precise agriculture, intelligent transportation, earth surface object recognition, and so on. To obtain more detailed information, we design a reference-based super-resolution reconstruction network. Firstly, the coarse-to-fine feature matching strategy is adopted for the features of the input image. Global coarse matching is performed on the center patch of each block, and then pixel-level local fine matching is performed on the edge patches of the block. This both reduces the amount of computation and improves the matching accuracy. A threshold is set to determine whether the feature matching results meet the criteria for feature transfer. Finally, different scale features are fused through several convolutional layers and sampling operations, obtaining the reconstruction features after a fourfold increase in resolution. The ultimate super-resolution image is generated through a decoder. We have performed training and testing on remote sensing datasets. Compared to the current state-of-the-art methods, our proposed method is visually more details and outperforms other methods in terms of objective evaluation metrics.

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IROS Conference 2024 Conference Paper

SGOR: Outlier Removal by Leveraging Semantic and Geometric Information for Robust Point Cloud Registration

Guiyu Zhao
Zhentao Guo
Hongbin Ma

In this paper, we introduce a new outlier removal method that fully leverages geometric and semantic information, to achieve robust registration. Current semantic-based registration methods only use semantics for point-to-point or instance semantic correspondence generation, which has two problems. First, these methods are highly dependent on the correctness of semantics. They perform poorly in scenarios with incorrect semantics and sparse semantics. Second, the use of semantics is limited only to the correspondence generation, resulting in bad performance in the weak geometry scene. To solve these problems, on the one hand, we propose secondary ground segmentation and loose semantic consistency based on regional voting. It improves the robustness to semantic correctness by reducing the dependence on single-point semantics. On the other hand, we propose semantic-geometric consistency for outlier removal, which makes full use of semantic information and significantly improves the quality of correspondences. In addition, a two-stage hypothesis verification is proposed, which solves the problem of incorrect transformation selection in the weak geometry scene. In the outdoor dataset, our method demonstrates superior performance, boosting a 22. 5 percentage points improvement in registration recall and achieving better robustness under various conditions. Our code is available.

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EAAI Journal 2022 Journal Article

Data-driven model for accommodation of faulty angle of attack sensor measurements in fixed winged aircraft

Bemnet Wondimagegnehu Mersha
Hongbin Ma

On March 10, 2019, Ethiopian Airlines’ Boeing 737-8 MAX nose-dived and crashed shortly after takeoff in the south-east of Addis Ababa, near Ejere Town. The cause of the crash was a faulty angle of attack (AOA) sensor. In the past, many aircraft accidents were associated with faulty AOA sensors. The literature uses triplex and duplex AOA sensor fault detection, isolation, and accommodation (SFDIA). The triplex voting mechanism uses three AOA sensors. A critical problem with the triplex method is that the consolidated value inherently depends on the sensor measurements. This problem makes fault detection and isolation susceptible to simultaneous failure. The duplex fault detection and isolation uses two sensors, reducing cost and providing lower protection. We propose using two AOA sensors and a virtual AOA sensor for faulty AOA SFDIA. The proposed faulty AOA sensor detection and isolation algorithm is based on conventional residual analysis with a fixed threshold for faulty AOA sensor detection and isolation. The virtual sensor is a data-driven model based on a recurrent neural network (RNN) for AOA accommodation. We use a combination of simple RNN (sRNN) and gated recurrent units (GRU). The model aims to effectively use the encoder–decoder behavior of the GRU for better AOA accommodation in the case of faulty AOA measurement, faulty velocity measurement, and faulty pitch rate measurement. Test results show that the proposed method can detect, isolate, and accommodate faulty AOA sensors with a lower number of false alarms than a model that uses only long-term memory (LSTM).

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IROS Conference 2015 Conference Paper

Shared control for teleoperation enhanced by autonomous obstacle avoidance of robot manipulator

Xinyu Wang 0018
Chenguang Yang 0001
Hongbin Ma
Long Cheng 0001

In this paper, a human robot shared control strategy is developed and tested on a Baxter robot. Using the proposed method, the human operator only needs to consider the motion of the end-effector of the manipulator, while the manipulator will avoid obstacle by itself without sacrificing the end effector motion performance. An improved obstacle avoidance strategy based on the joint space redundancy of the manipulator is designed. A dimension reduction method is presented to solve the over defined problem of avoiding velocity to achieve a more efficient use of the redundancy. By employment of an artificial parallel system of the teleoperate manipulator and the task switching weighting factor, the proposed control method enable the robot restoring back to the commanded pose smoothly when the obstacle is removed. By implementing the dimension reduction method, the trajectory of each joint of the manipulator can be controlled at the same time to achieve the restoring task. Thus, the proposed control method can eliminate the impact of the obstacle on the remaining task. Satisfactory experiment results demonstrate the effectiveness of the proposed methods.

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