New submissions for Wed, 23 Feb 22

[zhuhu00]

Keyword: SLAM

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Keyword: Visual inertial

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Keyword: livox

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Keyword: loam

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Keyword: Visual inertial odometry

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Keyword: lidar

Estimation of Looming from LiDAR

• Authors: Juan D. Yepes, Daniel Raviv
• Subjects: Computer Vision and Pattern Recognition (cs.CV)
• Arxiv link: https://arxiv.org/abs/2202.10972
• Pdf link: https://arxiv.org/pdf/2202.10972
• Abstract Looming, traditionally defined as the relative expansion of objects in the observer's retina, is a fundamental visual cue for perception of threat and can be used to accomplish collision free navigation. The measurement of the looming cue is not only limited to vision, and can also be obtained from range sensors like LiDAR (Light Detection and Ranging). In this article we present two methods that process raw LiDAR data to estimate the looming cue. Using looming values we show how to obtain threat zones for collision avoidance tasks. The methods are general enough to be suitable for any six-degree-of-freedom motion and can be implemented in real-time without the need for fine matching, point-cloud registration, object classification or object segmentation. Quantitative results using the KITTI dataset shows advantages and limitations of the methods.

  Robust and Online LiDAR-inertial Initialization

• Authors: Fangcheng Zhu, Yunfan Ren, Fu Zhang
• Subjects: Robotics (cs.RO)
• Arxiv link: https://arxiv.org/abs/2202.11006
• Pdf link: https://arxiv.org/pdf/2202.11006
• Abstract For most LiDAR-inertial odometry, accurate initial state, including temporal offset and extrinsic transformation between LiDAR and 6-axis IMUs, play a significant role and are often considered as prerequisites. However, such information may not be always available in customized LiDAR-inertial systems. In this paper, we propose a full and online LiDAR-inertial system initialization process that calibrates the temporal offset and extrinsic parameter between LiDARs and IMUs, and also the gravity vector and IMU bias by aligning the state estimated from LiDAR measurements with that measured by IMU. We implement the proposed method as an initialization module, which, if enabled, automatically detects the degree of excitation of the collected data and calibrate, on-the-fly, the temporal offset, extrinsic, gravity vector, and IMU bias, which are then used as high-quality initial state values for online LiDAR-inertial odometry systems. Experiments conducted with different types of LiDARs and LiDAR-inertial combinations show the robustness, adaptability and efficiency of our initialization method. The implementation of our LiDAR-inertial initialization procedure and test data are open-sourced on Github and also integrated into a state-of-the-art LiDAR-inertial odometry system FAST-LIO2.

  3D ToF LiDAR in Mobile Robotics: A Review

• Authors: Tao Yang, You Li, Cheng Zhao, Dexin Yao, Guanyin Chen, Li Sun, Tomas Krajnik, Zhi Yan
• Subjects: Robotics (cs.RO)
• Arxiv link: https://arxiv.org/abs/2202.11025
• Pdf link: https://arxiv.org/pdf/2202.11025
• Abstract In the past ten years, the use of 3D Time-of-Flight (ToF) LiDARs in mobile robotics has grown rapidly. Based on our accumulation of relevant research, this article systematically reviews and analyzes the use 3D ToF LiDARs in research and industrial applications. The former includes object detection, robot localization, long-term autonomy, LiDAR data processing under adverse weather conditions, and sensor fusion. The latter encompasses service robots, assisted and autonomous driving, and recent applications performed in response to public health crises. We hope that our efforts can effectively provide readers with relevant references and promote the deployment of existing mature technologies in real-world systems.

  Keyword: loop detection

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  Keyword: autonomous driving

  3D ToF LiDAR in Mobile Robotics: A Review

• Authors: Tao Yang, You Li, Cheng Zhao, Dexin Yao, Guanyin Chen, Li Sun, Tomas Krajnik, Zhi Yan
• Subjects: Robotics (cs.RO)
• Arxiv link: https://arxiv.org/abs/2202.11025
• Pdf link: https://arxiv.org/pdf/2202.11025
• Abstract In the past ten years, the use of 3D Time-of-Flight (ToF) LiDARs in mobile robotics has grown rapidly. Based on our accumulation of relevant research, this article systematically reviews and analyzes the use 3D ToF LiDARs in research and industrial applications. The former includes object detection, robot localization, long-term autonomy, LiDAR data processing under adverse weather conditions, and sensor fusion. The latter encompasses service robots, assisted and autonomous driving, and recent applications performed in response to public health crises. We hope that our efforts can effectively provide readers with relevant references and promote the deployment of existing mature technologies in real-world systems.

  Keyword: mapping

  Self-Evolutionary Clustering

• Authors: Hanxuan Wang, Na Lu, Qinyang Liu
• Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
• Arxiv link: https://arxiv.org/abs/2202.10505
• Pdf link: https://arxiv.org/pdf/2202.10505
• Abstract Deep clustering outperforms conventional clustering by mutually promoting representation learning and cluster assignment. However, most existing deep clustering methods suffer from two major drawbacks. First, most cluster assignment methods are based on simple distance comparison and highly dependent on the target distribution generated by a handcrafted nonlinear mapping. These facts largely limit the possible performance that deep clustering methods can reach. Second, the clustering results can be easily guided towards wrong direction by the misassigned samples in each cluster. The existing deep clustering methods are incapable of discriminating such samples. To address these issues, a novel modular Self-Evolutionary Clustering (Self-EvoC) framework is constructed, which boosts the clustering performance by classification in a self-supervised manner. Fuzzy theory is used to score the sample membership with probability which evaluates the intermediate clustering result certainty of each sample. Based on which, the most reliable samples can be selected and augmented. The augmented data are employed to fine-tune an off-the-shelf deep network classifier with the labels from the clustering, which results in a model to generate the target distribution. The proposed framework can efficiently discriminate sample outliers and generate better target distribution with the assistance of self-supervised classifier. Extensive experiments indicate that the Self-EvoC remarkably outperforms state-of-the-art deep clustering methods on three benchmark datasets.

  No-Regret Learning in Partially-Informed Auctions

• Authors: Wenshuo Guo, Michael I. Jordan, Ellen Vitercik
• Subjects: Machine Learning (cs.LG); Computer Science and Game Theory (cs.GT)
• Arxiv link: https://arxiv.org/abs/2202.10606
• Pdf link: https://arxiv.org/pdf/2202.10606
• Abstract Auctions with partially-revealed information about items are broadly employed in real-world applications, but the underlying mechanisms have limited theoretical support. In this work, we study a machine learning formulation of these types of mechanisms, presenting algorithms that are no-regret from the buyer's perspective. Specifically, a buyer who wishes to maximize his utility interacts repeatedly with a platform over a series of $T$ rounds. In each round, a new item is drawn from an unknown distribution and the platform publishes a price together with incomplete, "masked" information about the item. The buyer then decides whether to purchase the item. We formalize this problem as an online learning task where the goal is to have low regret with respect to a myopic oracle that has perfect knowledge of the distribution over items and the seller's masking function. When the distribution over items is known to the buyer and the mask is a SimHash function mapping $\mathbb{R}^d$ to ${0,1}^{\ell}$, our algorithm has regret $\tilde {\mathcal{O}}((Td\ell)^{\frac{1}{2}})$. In a fully agnostic setting when the mask is an arbitrary function mapping to a set of size $n$, our algorithm has regret $\tilde {\mathcal{O}}(T^{\frac{3}{4}}n^{\frac{1}{2}})$. Finally, when the prices are stochastic, the algorithm has regret $\tilde {\mathcal{O}}((Tn)^{\frac{1}{2}})$.

  Hidden bawls, whispers, and yelps: can text be made to sound more than just its words?

• Authors: Caluã de Lacerda Pataca, Paula Dornhofer Paro Costa
• Subjects: Human-Computer Interaction (cs.HC); Sound (cs.SD); Audio and Speech Processing (eess.AS)
• Arxiv link: https://arxiv.org/abs/2202.10631
• Pdf link: https://arxiv.org/pdf/2202.10631
• Abstract Whether a word was bawled, whispered, or yelped, captions will typically represent it in the same way. If they are your only way to access what is being said, subjective nuances expressed in the voice will be lost. Since so much of communication is carried by these nuances, we posit that if captions are to be used as an accurate representation of speech, embedding visual representations of paralinguistic qualities into captions could help readers use them to better understand speech beyond its mere textual content. This paper presents a model for processing vocal prosody (its loudness, pitch, and duration) and mapping it into visual dimensions of typography (respectively, font-weight, baseline shift, and letter-spacing), creating a visual representation of these lost vocal subtleties that can be embedded directly into the typographical form of text. An evaluation was carried out where participants were exposed to this speech-modulated typography and asked to match it to its originating audio, presented between similar alternatives. Participants (n=117) were able to correctly identify the original audios with an average accuracy of 65%, with no significant difference when showing them modulations as animated or static text. Additionally, participants' comments showed their mental models of speech-modulated typography varied widely.

  JAMES: Job Title Mapping with Multi-Aspect Embeddings and Reasoning

• Authors: Michiharu Yamashita, Jia Tracy Shen, Hamoon Ekhtiari, Thanh Tran, Dongwon Lee
• Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
• Arxiv link: https://arxiv.org/abs/2202.10739
• Pdf link: https://arxiv.org/pdf/2202.10739
• Abstract One of the most essential tasks needed for various downstream tasks in career analytics (e.g., career trajectory analysis, job mobility prediction, and job recommendation) is Job Title Mapping (JTM), where the goal is to map user-created (noisy and non-standard) job titles to predefined and standard job titles. However, solving JTM is domain-specific and non-trivial due to its inherent challenges: (1) user-created job titles are messy, (2) different job titles often overlap their job requirements, (3) job transition trajectories are inconsistent, and (4) the number of job titles in real world applications is large-scale. Toward this JTM problem, in this work, we propose a novel solution, named as JAMES, that constructs three unique embeddings of a target job title: topological, semantic, and syntactic embeddings, together with multi-aspect co-attention. In addition, we employ logical reasoning representations to collaboratively estimate similarities between messy job titles and standard job titles in the reasoning space. We conduct comprehensive experiments against ten competing models on the large-scale real-world dataset with more than 350,000 job titles. Our results show that JAMES significantly outperforms the best baseline by 10.06% in Precision@10 and by 17.52% in NDCG@10, respectively.

  A Benchmark Comparison of Learned Control Policies for Agile Quadrotor Flight

• Authors: Elia Kaufmann, Leonard Bauersfeld, Davide Scaramuzza
• Subjects: Robotics (cs.RO)
• Arxiv link: https://arxiv.org/abs/2202.10796
• Pdf link: https://arxiv.org/pdf/2202.10796
• Abstract Quadrotors are highly nonlinear dynamical systems that require carefully tuned controllers to be pushed to their physical limits. Recently, learning-based control policies have been proposed for quadrotors, as they would potentially allow learning direct mappings from high-dimensional raw sensory observations to actions. Due to sample inefficiency, training such learned controllers on the real platform is impractical or even impossible. Training in simulation is attractive but requires to transfer policies between domains, which demands trained policies to be robust to such domain gap. In this work, we make two contributions: (i) we perform the first benchmark comparison of existing learned control policies for agile quadrotor flight and show that training a control policy that commands body-rates and thrust results in more robust sim-to-real transfer compared to a policy that directly specifies individual rotor thrusts, (ii) we demonstrate for the first time that such a control policy trained via deep reinforcement learning can control a quadrotor in real-world experiments at speeds over 45km/h.

  Registered Report: A Laboratory Experiment on Using Different Financial-Incentivization Schemes in Software-Engineering Experimentation

• Authors: Jacob Krüger (1), Gül Çalıklı (2), Dmitri Bershadskyy (3), Robert Heyer (3), Sarah Zabel (3 and 4), Siegmar Otto (3 and 4) ((1) Ruhr-University Bochum Germany (2) University of Glasgow UK, (3) Otto-von-Guericke University Magdeburg Germany, (4) University of Hohenheim Germany)
• Subjects: Software Engineering (cs.SE)
• Arxiv link: https://arxiv.org/abs/2202.10985
• Pdf link: https://arxiv.org/pdf/2202.10985
• Abstract Empirical studies in software engineering are often conducted with open-source developers or in industrial collaborations. Seemingly, this resulted in few experiments using financial incentives (e.g., money, vouchers) as a strategy to motivate the participants' behavior; which is typically done in other research communities, such as economics or psychology. Even the current version of the SIGSOFT Empirical Standards does mention payouts for completing surveys only, but not for mimicking the real-world or motivating realistic behavior during experiments. So, there is a lack of understanding regarding whether financial incentives can or cannot be useful for software-engineering experimentation. To tackle this problem, we plan a survey based on which we will conduct a controlled laboratory experiment. Precisely, we will use the survey to elicit incentivization schemes we will employ as (up to) four payoff functions (i.e., mappings of choices or performance in an experiment to a monetary payment) during a code-review task in the experiment: (1) a scheme that employees prefer, (2) a scheme that is actually employed, (3) a scheme that is performance-independent, and (4) a scheme that mimics an open-source scenario. Using a between-subject design, we aim to explore how the different schemes impact the participants' performance. Our contributions help understand the impact of financial incentives on developers in experiments as well as real-world scenarios, guiding researchers in designing experiments and organizations in compensating developers.

  RMF-Owl: A Collision-Tolerant Flying Robot for Autonomous Subterranean Exploration

• Authors: Paolo De Petris, Huan Nguyen, Mihir Dharmadhikari, Mihir Kulkarni, Nikhil Khedekar, Frank Mascarich, Kostas Alexis
• Subjects: Robotics (cs.RO)
• Arxiv link: https://arxiv.org/abs/2202.11055
• Pdf link: https://arxiv.org/pdf/2202.11055
• Abstract This work presents the design, hardware realization, autonomous exploration and object detection capabilities of RMF-Owl, a new collision-tolerant aerial robot tailored for resilient autonomous subterranean exploration. The system is custom built for underground exploration with focus on collision tolerance, resilient autonomy with robust localization and mapping, alongside high-performance exploration path planning in confined, obstacle-filled and topologically complex underground environments. Moreover, RMF-Owl offers the ability to search, detect and locate objects of interest which can be particularly useful in search and rescue missions. A series of results from field experiments are presented in order to demonstrate the system's ability to autonomously explore challenging unknown underground environments.

  Keyword: localization

  3D ToF LiDAR in Mobile Robotics: A Review

• Authors: Tao Yang, You Li, Cheng Zhao, Dexin Yao, Guanyin Chen, Li Sun, Tomas Krajnik, Zhi Yan
• Subjects: Robotics (cs.RO)
• Arxiv link: https://arxiv.org/abs/2202.11025
• Pdf link: https://arxiv.org/pdf/2202.11025
• Abstract In the past ten years, the use of 3D Time-of-Flight (ToF) LiDARs in mobile robotics has grown rapidly. Based on our accumulation of relevant research, this article systematically reviews and analyzes the use 3D ToF LiDARs in research and industrial applications. The former includes object detection, robot localization, long-term autonomy, LiDAR data processing under adverse weather conditions, and sensor fusion. The latter encompasses service robots, assisted and autonomous driving, and recent applications performed in response to public health crises. We hope that our efforts can effectively provide readers with relevant references and promote the deployment of existing mature technologies in real-world systems.

  RMF-Owl: A Collision-Tolerant Flying Robot for Autonomous Subterranean Exploration

• Authors: Paolo De Petris, Huan Nguyen, Mihir Dharmadhikari, Mihir Kulkarni, Nikhil Khedekar, Frank Mascarich, Kostas Alexis
• Subjects: Robotics (cs.RO)
• Arxiv link: https://arxiv.org/abs/2202.11055
• Pdf link: https://arxiv.org/pdf/2202.11055
• Abstract This work presents the design, hardware realization, autonomous exploration and object detection capabilities of RMF-Owl, a new collision-tolerant aerial robot tailored for resilient autonomous subterranean exploration. The system is custom built for underground exploration with focus on collision tolerance, resilient autonomy with robust localization and mapping, alongside high-performance exploration path planning in confined, obstacle-filled and topologically complex underground environments. Moreover, RMF-Owl offers the ability to search, detect and locate objects of interest which can be particularly useful in search and rescue missions. A series of results from field experiments are presented in order to demonstrate the system's ability to autonomously explore challenging unknown underground environments.

  Constrained Visual-Inertial Localization With Application And Benchmark in Laparoscopic Surgery

• Authors: Regine Hartwig, Daniel Ostler, Jean-Claude Rosenthal, Hubertus Feußner, Dirk Wilhelm, Dirk Wollherr
• Subjects: Computer Vision and Pattern Recognition (cs.CV)
• Arxiv link: https://arxiv.org/abs/2202.11075
• Pdf link: https://arxiv.org/pdf/2202.11075
• Abstract We propose a novel method to tackle the visual-inertial localization problem for constrained camera movements. We use residuals from the different modalities to jointly optimize a global cost function. The residuals emerge from IMU measurements, stereoscopic feature points, and constraints on possible solutions in SE(3). In settings where dynamic disturbances are frequent, the residuals reduce the complexity of the problem and make localization feasible. We verify the advantages of our method in a suitable medical use case and produce a dataset capturing a minimally invasive surgery in the abdomen. Our novel clinical dataset MITI is comparable to state-of-the-art evaluation datasets, contains calibration and synchronization and is available at https://mediatum.ub.tum.de/1621941.