LOG-LIO: A LiDAR-Inertial Odometry with Efficient Local Geometric Information Estimation

Local geometric information, i.e. normal and distribution of points, is crucial for LiDAR-based simultaneous localization and mapping (SLAM) because it provides constraints for data association, which further determines the direction of optimization and ultimately affects the accuracy of localization. However, estimating normal and distribution of points are time-consuming tasks even with the assistance of kdtree or volumetric maps. To achieve fast normal estimation, we look into the structure of LiDAR scan and propose a ring-based fast approximate least squares (Ring FALS) method. With the Ring structural information, estimating the normal requires only the range information of the points when a new scan arrives. To efficiently estimate the distribution of points, we extend the ikd-tree to manage the map in voxels and update the distribution of points in each voxel incrementally while maintaining its consistency with the normal estimation. We further fix the distribution after its convergence to balance the time consumption and the correctness of representation. Based on the extracted and maintained local geometric information, we devise a robust and accurate hierarchical data association scheme where point-to-surfel association is prioritized over point-to-plane. Extensive experiments on diverse public datasets demonstrate the advantages of our system compared to other state-of-the-art methods. Our open source implementation is available at https://github.com/tiev-tongji/LOG-LIO.Comment: 8 pages, 4 figure

Scale Estimation with Dual Quadrics for Monocular Object SLAM

The scale ambiguity problem is inherently unsolvable to monocular SLAM without the metric baseline between moving cameras. In this paper, we present a novel scale estimation approach based on an object-level SLAM system. To obtain the absolute scale of the reconstructed map, we derive a nonlinear optimization method to make the scaled dimensions of objects conforming to the distribution of their sizes in the physical world, without relying on any prior information of gravity direction. We adopt the dual quadric to represent objects for its ability to fit objects compactly and accurately. In the proposed monocular object-level SLAM system, dual quadrics are fastly initialized based on constraints of 2-D detections and fitted oriented bounding box and are further optimized to provide reliable dimensions for scale estimation.Comment: 8 pages, 6 figures, accepted by IROS202

Analysis of the Willingness and Factors Influencing the Residents to Choose Between Chinese Medicine and Western Medicine under the New Coronavirus Pandemic: A Study in Zhejiang Province Community Health Service Center

Objective: To understand the willingness of Chinese residents to choose between Chinese and Western medicine in the face of sudden outbreak, this study aims to investigate and analyze the willingness and factors influencing Chinese residents (taking Zhejiang Province as an example) to choose between Chinese and Western medicine under the new coronavirus pandemic. Methods: The present study performed a large-scale cross-sectional online survey among 666 random residents in Zhejiang Province. We used questionnaires to investigate the feedback form from residents seeking medical care. In addition, a multivariate logistic regression model was used to analyze the influence of gender, education, medical reimbursement, and age on the choice of Chinese and Western medicine. Results: Among the patients with mild disease, 55.9% patients chose traditional Chinese medicine, while 44.1% chose Western medicine. Moreover, the proportion of patients with severe diseases who chose traditional Chinese medicine was 7.0%, while the rate of Western medicine was 93.0%. Among the patients suffering from mild diseases, the proportion of men who chose traditional Chinese medicine (46.2%) was lower than that of women (53.8%). The usage of Chinese medicine was preferred among residents of all ages, income levels, and educational backgrounds. A total of 93.0% of patients who chose Western medicine for treatment were severely ill, and the residents with severe diseases preferred Western medicine to Chinese medicine. People with high education and young were more inclined toward Western medicine for treatment compared with Chinese medicine. It was noted that people paid most attention to the medical insurance reimbursement ratio, followed by the distance between the medical institution and the place of residence. Conclusion: The acceptance of Chinese medicine among patients has generally increased; however, gender, educational background, and income still exert a great influence on the choice between Chinese and Western medicine

Domain Adaptation with Incomplete Target Domains

Domain adaptation, as a task of reducing the annotation cost in a target domain by exploiting the existing labeled data in an auxiliary source domain, has received a lot of attention in the research community. However, the standard domain adaptation has assumed perfectly observed data in both domains, while in real world applications the existence of missing data can be prevalent. In this paper, we tackle a more challenging domain adaptation scenario where one has an incomplete target domain with partially observed data. We propose an Incomplete Data Imputation based Adversarial Network (IDIAN) model to address this new domain adaptation challenge. In the proposed model, we design a data imputation module to fill the missing feature values based on the partial observations in the target domain, while aligning the two domains via deep adversarial adaption. We conduct experiments on both cross-domain benchmark tasks and a real world adaptation task with imperfect target domains. The experimental results demonstrate the effectiveness of the proposed method

Replay-enhanced Continual Reinforcement Learning

Replaying past experiences has proven to be a highly effective approach for averting catastrophic forgetting in supervised continual learning. However, some crucial factors are still largely ignored, making it vulnerable to serious failure, when used as a solution to forgetting in continual reinforcement learning, even in the context of perfect memory where all data of previous tasks are accessible in the current task. On the one hand, since most reinforcement learning algorithms are not invariant to the reward scale, the previously well-learned tasks (with high rewards) may appear to be more salient to the current learning process than the current task (with small initial rewards). This causes the agent to concentrate on those salient tasks at the expense of generality on the current task. On the other hand, offline learning on replayed tasks while learning a new task may induce a distributional shift between the dataset and the learned policy on old tasks, resulting in forgetting. In this paper, we introduce RECALL, a replay-enhanced method that greatly improves the plasticity of existing replay-based methods on new tasks while effectively avoiding the recurrence of catastrophic forgetting in continual reinforcement learning. RECALL leverages adaptive normalization on approximate targets and policy distillation on old tasks to enhance generality and stability, respectively. Extensive experiments on the Continual World benchmark show that RECALL performs significantly better than purely perfect memory replay, and achieves comparable or better overall performance against state-of-the-art continual learning methods.Comment: Accepted by Transactions on Machine Learning Research 202

A Highly Efficient Dual Rotating Disks Photocatalytic Fuel Cell with Wedged Surface TiO2 Nanopore Anode and Hemoglobin Film Cathode

In this study, a dual rotating-disk photocatalytic fuel cell using TiO2 on Ti plate with a wedged surface as the anode and hemoglobin (Hb) on graphite as the cathode was investigated and found to show excellent performance of simultaneous organic pollutant degradation and electricity generation. This study is based on a well-developed photocatalytic fuel cell equipped with dual rotating disks for wastewater treatment that we developed previously, and the innovation of this new device is using a hemoglobin on graphite cathode for in situ hydrogen peroxide (H2O2) generation. The result proved with confidence that H2O2 was generated in situ on a cathode surface with the exited electron transferred from organic oxidation in a photoanodic half cell, and the organic pollutants were removed by the reaction with H2O2 and OH in a cathodic half cell. This design uses the invalid excited electron from the photoanode and enhances the overall performance of Rhodamine B degradation compared with the cells using the cathode without Hb. Compared with traditional photocatalytic reactors, the photocatalytic fuel cell developed above shows much better utilization efficiency of incident light and a higher degradation performance of organic pollutants and a larger photocurrent