Haowen Lai

Abstract will be available after published.

In the era of advancing autonomous driving and increasing reliance on geospatial information, high-precision mapping not only demands accuracy but also flexible construction. Current approaches mainly rely on expensive mapping devices, which are time consuming for city-scale map construction and vulnerable to erroneous data associations without accurate GPS assistance. In this article, we present AutoMerge, a novel framework for merging large-scale maps that surpasses these limitations, which: 1) provides robust place recognition performance despite differences in both translation and viewpoint; 2) is capable of identifying and discarding incorrect loop closures caused by perceptual aliasing; and 3) effectively associates and optimizes large-scale and numerous map segments in the real-world scenario. AutoMerge utilizes multiperspective fusion and adaptive loop closure detection for accurate data associations, and it uses incremental merging to assemble large maps from individual trajectory segments given in random order and with no initial estimations. Furthermore, AutoMerge performs pose graph optimization after assembling the segments to smooth the merged map globally. We demonstrate AutoMerge on both city-scale merging (120 km) and campus-scale repeated merging (4.5 km x 8). The experiments show that AutoMerge: 1) surpasses the second- and third-best methods by 0.9% and 6.5% recall in segment retrieval; 2) achieves comparable 3-D mapping accuracy for 120-km large-scale map assembly; and 3) and is robust to temporally spaced revisits. To our knowledge, AutoMerge is the first mapping approach to merge hundreds of kilometers of individual segments without using GPS.

@article{yin2023automerge,
  title={Automerge: A Framework for Map Assembling
  	 and Smoothing in City-Scale Environments},
  author={Yin, Peng and Zhao, Shiqi and Lai, Haowen
	 and Ge, Ruohai and Zhang, Ji and Choset, Howie
	 and Scherer, Sebastian},
  journal={IEEE Transactions on Robotics},
  volume={39},
  number={5},
  pages={3686-3704},
  year={2023}
}
			

This article studies a planar Homicidal Chauffeur reach-avoid differential game, where the pursuer is a Dubins car and the evader has simple motion. The pursuer aims to protect a goal region from the evader. The game is solved in an analytical approach instead of solving Hamilton-Jacobi-Isaacs equations numerically. First, an evasion region is introduced, based on which a pursuit strategy guaranteeing the winning of a simple-motion pursuer under specific conditions is proposed. Motivated by the simple-motion pursuer, a strategy for a Dubins-car pursuer is proposed when the pursuer-evader configuration satisfies separation condition (SC) and interception orientation (IO) . The necessary and sufficient condition on capture radius, minimum turning radius, and speed ratio to guarantee the pursuit winning is derived. When the IO is deviated (Non-IO), a heading adjustment pursuit strategy is proposed, and the condition to achieve IO within a finite time is given. Based on it, a two-step pursuit strategy is proposed for the SC and Non-IO case. A nonconvex optimization problem is introduced to give a condition guaranteeing the winning of the pursuer. A polynomial equation gives a lower bound of the nonconvex problem, providing a sufficient and efficient pursuit winning condition. Finally, we extend to multiplayer games by collecting pairwise outcomes for pursuer-evader matchings. Simulations are provided to illustrate the theoretical results.

@article{yan2023homicidal,
  title={Homicidal Chauffeur Reach-Avoid Games via
     Guaranteed Winning Strategies},
  author={Yan, Rui and Deng, Ruiliang and Lai, Haowen and
     Zhang, Weixian and Shi, Zongying and Zhong, Yisheng},
  journal={IEEE Transactions on Automatic Control},
  volume={69},
  number={4},
  pages={2367-2382},
  year={2023}
}
		    

Recent years have witnessed the increasing application of place recognition in various environments, such as city roads, large buildings, and a mix of indoor and outdoor places. This task, however, still remains challenging due to the limitations of different sensors and the changing appearance of environments. Current works only consider the use of individual sensors, or simply combine different sensors, ignoring the fact that the importance of different sensors varies as the environment changes. In this paper, an adaptive weighting visual-LiDAR fusion method, named AdaFusion, is proposed to learn the weights for both images and point cloud features. Features of these two modalities are thus contributed differently according to the current environmental situation. The learning of weights is achieved by the attention branch of the network, which is then fused with the multi-modality feature extraction branch. Furthermore, to better utilize the potential relationship between images and point clouds, we design a twostage fusion approach to combine the 2D and 3D attention. Our work is tested on two public datasets, and experiments show that the adaptive weights help improve recognition accuracy and system robustness to varying environments.

@article{lai2022adafusion,
  title={Adafusion: Visual-lidar Fusion with Adaptive
	 Weights for Place Recognition},
  author={Lai, Haowen and Yin, Peng and Scherer, Sebastian},
  journal={IEEE Robotics and Automation Letters},
  volume={7},
  number={4},
  pages={12038--12045},
  year={2022},
  publisher={IEEE}
}
			

Merging multiple maps is necessary when the task of mapping a large area is divided and assigned to different robots. Unlike scanned or reconstructed models, point cloud maps built by SLAM are usually sparser and contain much noise, making current global registration methods failed. In this paper, we focus on the global registration of point cloud maps which have low-overlap regions and no prior relative transformation between each other, so that maps can still be merged in GPS-denied environment. Our method adopts a pipeline of feature matching, where key points are first selected evenly within voxel grids and then used for getting feature descriptors. After matching for correspondences, a RANSAC based registration gives the rough transformation between maps, which can further be refined by ICP. To improve the accuracy of correspondences, we specifically introduce a keypoint clustering and classification method. Except for 3D point cloud maps as inputs, no other information is needed. Experiments on the campus show that our approach successfully deals with large area with similar structures.

@inproceedings{liang2022global,
  title={Global Registration of Point Cloud Maps with 
	 Low-overlap Regions},
  author={Liang, Wenhao and Lai, Haowen and Shi, Zongying 
	 and Zhong, Yisheng},
  booktitle={2022 41st Chinese Control Conference (CCC)},
  pages={3743--3748},
  year={2022},
  organization={IEEE}
}
			

This paper considers a one-defender-one-attacker reach-avoid differential game (DG) in the plane which is split by a straight line into a goal region and a play region. The attacker aims at entering the goal region from the play region without being captured, while the defender tries to capture the attacker in the play region. We focus on the defense problem where the defender is a Dubins car and the attacker is a simple-motion model. First, a controller is proposed for the defender to track a reference heading which is derived from an evasion space (ES)-based defense strategy. Then, an upper bound for the time derivative of the reference heading is computed. Based on it, we show that the defender can succeed to track the reference heading within a finite time. Finally, both simulation and experiment examples are provided, where a vision-based re-localization method is used for the experiment to deal with the coordinate inconsistency problem.

@inproceedings{lai2021ReachAvoid,
  author={Lai, Haowen and Yan, Rui and Zhang, Weixian and 
          Shi, Zongying and Zhong, Yisheng},
  booktitle={2021 60th IEEE Conference on Decision and
             Control (CDC)}, 
  title={Reach-Avoid Differential Games via Finite-Time
         Heading Tracking}, 
  year={2021},
  pages={1656-1662},
  doi={10.1109/CDC45484.2021.9683266}
}
			

Pursuit-evasion (PE) differential games are widely studied due to their enormous application prospects in robotics. Most of current works, however, focus on the theory and simulation, while even for some experiments the global information is directly acquired by external devices or just by sharing, which is far from the real scenarios. Motivated by this limitation, we propose a LiDAR-inertial based localization and perception method to estimate robot states such as position, pose and velocity required by high-level strategies. The LiDAR-inertial odometry (LIO) that utilizes data from 3D LiDAR and IMU provides self localization in an unknown environment. To locate other robots, a curve fitting based algorithm for 3D LiDAR point clouds is proposed, after which a sliding window average method is adopted to filter the noise in the perception results. Except for 3D LiDAR and IMU, no other sensors or communication is needed. Experiments are presented to illustrate the results.

@inproceedings{lai2021LiDAR,
  author={Lai, Haowen and Liang, Wenhao and Yan, Rui and 
          Shi, Zongying and Zhong, Yisheng},
  booktitle={2021 IEEE 40th Chinese Control Conference (CCC)}, 
  title={LiDAR-Inertial based Localization and Perception for 
         Indoor Pursuit-Evasion Differential Games}, 
  year={2021},
  pages={7468-7473},
  doi={10.23919/CCC52363.2021.9549330}
}
			

Nowadays plenty of pointer meters are used in the field of chemical industry and electrical power system. To avoid reading their indication manually, many algorithms based on computer vision have been proposed to read pointer meters automatically. These methods, however, are limited to meters whose scales are uniform, and their accuracy is vulnerable to the error in the recognition of a meter's center. In this paper, a novel automatic reading algorithm of pointer meters based on scale seeking is proposed to overcome the weaknesses of the existing methods. Differing from the popular angle-based methods, we obtain the indication of the meter by comparing the distances between the peak of pointer and its nearest scales. The position and values of all scales can be automatically acquired and inferred by using our scale seeking and value inference algorithms, which is independent of any prior information in a database. Experiments prove that the algorithm can be applied to both meters with uniform or non-uniform scales effectively.

@inproceedings{lai2019novel,
  author = {Lai, Haowen and Kang, Qi and Pan, Le and Cui, Can},
  booktitle = {2019 IEEE 15th International Conference on 
               Automation Science and Engineering (CASE)}, 
  title = {A Novel Scale Recognition Method for Pointer 
           Meters Adapted to Different Types and Shapes}, 
  year = {2019}, 
  pages={374-379}, 
  organization={IEEE}, 
  doi={10.1109/COASE.2019.8843107}
}