New submissions for Thu, 24 Feb 22

[zhuhu00]

Keyword: SLAM

Are We Ready for Robust and Resilient SLAM? A Framework For Quantitative Characterization of SLAM Datasets

• Authors: Islam Ali, Hong Zhang
• Subjects: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
• Arxiv link: https://arxiv.org/abs/2202.11312
• Pdf link: https://arxiv.org/pdf/2202.11312
• Abstract Reliability of SLAM systems is considered one of the critical requirements in many modern autonomous systems. This directed the efforts to developing many state-of-the-art systems, creating challenging datasets, and introducing rigorous metrics to measure SLAM system performance. However, the link between datasets and performance in the robustness/resilience context has rarely been explored. In order to fill this void, characterization the operating conditions of SLAM systems is essential in order to provide an environment for quantitative measurement of robustness and resilience. In this paper, we argue that for proper evaluation of SLAM performance, the characterization of SLAM datasets serves as a critical first step. The study starts by reviewing previous efforts for quantitative characterization of SLAM datasets. Then, the problem of perturbations characterization is discussed and the linkage to SLAM robustness/resilience is established. After that, we propose a novel, generic and extendable framework for quantitative analysis and comparison of SLAM datasets. Additionally, a description of different characterization parameters is provided. Finally, we demonstrate the application of our framework by presenting the characterization results of three SLAM datasets: KITTI, EuroC-MAV, and TUM-VI highlighting the level of insights achieved by the proposed framework.

  DL-SLOT: Dynamic Lidar SLAM and Object Tracking Based On Graph Optimization

• Authors: Xuebo Tian, Junqiao Zhao, Chen Ye
• Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI)
• Arxiv link: https://arxiv.org/abs/2202.11431
• Pdf link: https://arxiv.org/pdf/2202.11431
• Abstract Ego-pose estimation and dynamic object tracking are two key issues in an autonomous driving system. Two assumptions are often made for them, i.e. the static world assumption of simultaneous localization and mapping (SLAM) and the exact ego-pose assumption of object tracking, respectively. However, these assumptions are difficult to hold in highly dynamic road scenarios where SLAM and object tracking become correlated and mutually beneficial. In this paper, DL-SLOT, a dynamic Lidar SLAM and object tracking method is proposed. This method integrates the state estimations of both the ego vehicle and the static and dynamic objects in the environment into a unified optimization framework, to realize SLAM and object tracking (SLOT) simultaneously. Firstly, we implement object detection to remove all the points that belong to potential dynamic objects. Then, LiDAR odometry is conducted using the filtered point cloud. At the same time, detected objects are associated with the history object trajectories based on the time-series information in a sliding window. The states of the static and dynamic objects and ego vehicle in the sliding window are integrated into a unified local optimization framework. We perform SLAM and object tracking simultaneously in this framework, which significantly improves the robustness and accuracy of SLAM in highly dynamic road scenarios and the accuracy of objects' states estimation. Experiments on public datasets have shown that our method achieves better accuracy than A-LOAM.

  Keyword: Visual inertial

  There is no result

  Keyword: livox

  There is no result

  Keyword: loam

  DL-SLOT: Dynamic Lidar SLAM and Object Tracking Based On Graph Optimization

• Authors: Xuebo Tian, Junqiao Zhao, Chen Ye
• Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI)
• Arxiv link: https://arxiv.org/abs/2202.11431
• Pdf link: https://arxiv.org/pdf/2202.11431
• Abstract Ego-pose estimation and dynamic object tracking are two key issues in an autonomous driving system. Two assumptions are often made for them, i.e. the static world assumption of simultaneous localization and mapping (SLAM) and the exact ego-pose assumption of object tracking, respectively. However, these assumptions are difficult to hold in highly dynamic road scenarios where SLAM and object tracking become correlated and mutually beneficial. In this paper, DL-SLOT, a dynamic Lidar SLAM and object tracking method is proposed. This method integrates the state estimations of both the ego vehicle and the static and dynamic objects in the environment into a unified optimization framework, to realize SLAM and object tracking (SLOT) simultaneously. Firstly, we implement object detection to remove all the points that belong to potential dynamic objects. Then, LiDAR odometry is conducted using the filtered point cloud. At the same time, detected objects are associated with the history object trajectories based on the time-series information in a sliding window. The states of the static and dynamic objects and ego vehicle in the sliding window are integrated into a unified local optimization framework. We perform SLAM and object tracking simultaneously in this framework, which significantly improves the robustness and accuracy of SLAM in highly dynamic road scenarios and the accuracy of objects' states estimation. Experiments on public datasets have shown that our method achieves better accuracy than A-LOAM.

  Keyword: Visual inertial odometry

  There is no result

  Keyword: lidar

  Enabling Efficient Deep Convolutional Neural Network-based Sensor Fusion for Autonomous Driving

• Authors: Xiaoming Zeng, Zhendong Wang, Yang Hu
• Subjects: Computer Vision and Pattern Recognition (cs.CV)
• Arxiv link: https://arxiv.org/abs/2202.11231
• Pdf link: https://arxiv.org/pdf/2202.11231
• Abstract Autonomous driving demands accurate perception and safe decision-making. To achieve this, automated vehicles are now equipped with multiple sensors (e.g., camera, Lidar, etc.), enabling them to exploit complementary environmental context by fusing data from different sensing modalities. With the success of Deep Convolutional Neural Network(DCNN), the fusion between DCNNs has been proved as a promising strategy to achieve satisfactory perception accuracy. However, mainstream existing DCNN fusion schemes conduct fusion by directly element-wisely adding feature maps extracted from different modalities together at various stages, failing to consider whether the features being fused are matched or not. Therefore, we first propose a feature disparity metric to quantitatively measure the degree of feature disparity between the feature maps being fused. We then propose Fusion-filter as a feature-matching techniques to tackle the feature-mismatching issue. We also propose a Layer-sharing technique in the deep layer that can achieve better accuracy with less computational overhead. Together with the help of the feature disparity to be an additional loss, our proposed technologies enable DCNN to learn corresponding feature maps with similar characteristics and complementary visual context from different modalities to achieve better accuracy. Experimental results demonstrate that our proposed fusion technique can achieve better accuracy on KITTI dataset with less computational resources demand.

  DL-SLOT: Dynamic Lidar SLAM and Object Tracking Based On Graph Optimization

• Authors: Xuebo Tian, Junqiao Zhao, Chen Ye
• Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI)
• Arxiv link: https://arxiv.org/abs/2202.11431
• Pdf link: https://arxiv.org/pdf/2202.11431
• Abstract Ego-pose estimation and dynamic object tracking are two key issues in an autonomous driving system. Two assumptions are often made for them, i.e. the static world assumption of simultaneous localization and mapping (SLAM) and the exact ego-pose assumption of object tracking, respectively. However, these assumptions are difficult to hold in highly dynamic road scenarios where SLAM and object tracking become correlated and mutually beneficial. In this paper, DL-SLOT, a dynamic Lidar SLAM and object tracking method is proposed. This method integrates the state estimations of both the ego vehicle and the static and dynamic objects in the environment into a unified optimization framework, to realize SLAM and object tracking (SLOT) simultaneously. Firstly, we implement object detection to remove all the points that belong to potential dynamic objects. Then, LiDAR odometry is conducted using the filtered point cloud. At the same time, detected objects are associated with the history object trajectories based on the time-series information in a sliding window. The states of the static and dynamic objects and ego vehicle in the sliding window are integrated into a unified local optimization framework. We perform SLAM and object tracking simultaneously in this framework, which significantly improves the robustness and accuracy of SLAM in highly dynamic road scenarios and the accuracy of objects' states estimation. Experiments on public datasets have shown that our method achieves better accuracy than A-LOAM.

  Using Deep Reinforcement Learning with Automatic Curriculum earning for Mapless Navigation in Intralogistics

• Authors: Honghu Xue, Benedikt Hein, Mohamed Bakr, Georg Schildbach, Bengt Abel, Elmar Rueckert
• Subjects: Robotics (cs.RO); Machine Learning (cs.LG)
• Arxiv link: https://arxiv.org/abs/2202.11512
• Pdf link: https://arxiv.org/pdf/2202.11512
• Abstract We propose a deep reinforcement learning approach for solving a mapless navigation problem in warehouse scenarios. The automatic guided vehicle is equipped with LiDAR and frontal RGB sensors and learns to reach underneath the target dolly. The challenges reside in the sparseness of positive samples for learning, multi-modal sensor perception with partial observability, the demand for accurate steering maneuvers together with long training cycles. To address these points, we proposed NavACL-Q as an automatic curriculum learning together with distributed soft actor-critic. The performance of the learning algorithm is evaluated exhaustively in a different warehouse environment to check both robustness and generalizability of the learned policy. Results in NVIDIA Isaac Sim demonstrates that our trained agent significantly outperforms the map-based navigation pipeline provided by NVIDIA Isaac Sim in terms of higher agent-goal distances and relative orientations. The ablation studies also confirmed that NavACL-Q greatly facilitates the whole learning process and a pre-trained feature extractor manifestly boosts the training speed.

  Deep Bayesian ICP Covariance Estimation

• Authors: Andrea De Maio, Simon Lacroix
• Subjects: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
• Arxiv link: https://arxiv.org/abs/2202.11607
• Pdf link: https://arxiv.org/pdf/2202.11607
• Abstract Covariance estimation for the Iterative Closest Point (ICP) point cloud registration algorithm is essential for state estimation and sensor fusion purposes. We argue that a major source of error for ICP is in the input data itself, from the sensor noise to the scene geometry. Benefiting from recent developments in deep learning for point clouds, we propose a data-driven approach to learn an error model for ICP. We estimate covariances modeling data-dependent heteroscedastic aleatoric uncertainty, and epistemic uncertainty using a variational Bayesian approach. The system evaluation is performed on LiDAR odometry on different datasets, highlighting good results in comparison to the state of the art.

  Keyword: loop detection

  There is no result

  Keyword: autonomous driving

  Enabling Efficient Deep Convolutional Neural Network-based Sensor Fusion for Autonomous Driving

• Authors: Xiaoming Zeng, Zhendong Wang, Yang Hu
• Subjects: Computer Vision and Pattern Recognition (cs.CV)
• Arxiv link: https://arxiv.org/abs/2202.11231
• Pdf link: https://arxiv.org/pdf/2202.11231
• Abstract Autonomous driving demands accurate perception and safe decision-making. To achieve this, automated vehicles are now equipped with multiple sensors (e.g., camera, Lidar, etc.), enabling them to exploit complementary environmental context by fusing data from different sensing modalities. With the success of Deep Convolutional Neural Network(DCNN), the fusion between DCNNs has been proved as a promising strategy to achieve satisfactory perception accuracy. However, mainstream existing DCNN fusion schemes conduct fusion by directly element-wisely adding feature maps extracted from different modalities together at various stages, failing to consider whether the features being fused are matched or not. Therefore, we first propose a feature disparity metric to quantitatively measure the degree of feature disparity between the feature maps being fused. We then propose Fusion-filter as a feature-matching techniques to tackle the feature-mismatching issue. We also propose a Layer-sharing technique in the deep layer that can achieve better accuracy with less computational overhead. Together with the help of the feature disparity to be an additional loss, our proposed technologies enable DCNN to learn corresponding feature maps with similar characteristics and complementary visual context from different modalities to achieve better accuracy. Experimental results demonstrate that our proposed fusion technique can achieve better accuracy on KITTI dataset with less computational resources demand.

  DL-SLOT: Dynamic Lidar SLAM and Object Tracking Based On Graph Optimization

• Authors: Xuebo Tian, Junqiao Zhao, Chen Ye
• Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI)
• Arxiv link: https://arxiv.org/abs/2202.11431
• Pdf link: https://arxiv.org/pdf/2202.11431
• Abstract Ego-pose estimation and dynamic object tracking are two key issues in an autonomous driving system. Two assumptions are often made for them, i.e. the static world assumption of simultaneous localization and mapping (SLAM) and the exact ego-pose assumption of object tracking, respectively. However, these assumptions are difficult to hold in highly dynamic road scenarios where SLAM and object tracking become correlated and mutually beneficial. In this paper, DL-SLOT, a dynamic Lidar SLAM and object tracking method is proposed. This method integrates the state estimations of both the ego vehicle and the static and dynamic objects in the environment into a unified optimization framework, to realize SLAM and object tracking (SLOT) simultaneously. Firstly, we implement object detection to remove all the points that belong to potential dynamic objects. Then, LiDAR odometry is conducted using the filtered point cloud. At the same time, detected objects are associated with the history object trajectories based on the time-series information in a sliding window. The states of the static and dynamic objects and ego vehicle in the sliding window are integrated into a unified local optimization framework. We perform SLAM and object tracking simultaneously in this framework, which significantly improves the robustness and accuracy of SLAM in highly dynamic road scenarios and the accuracy of objects' states estimation. Experiments on public datasets have shown that our method achieves better accuracy than A-LOAM.

  Keyword: mapping

  Communicating Robot Conventions through Shared Autonomy

• Authors: Ananth Jonnavittula, Dylan P. Losey
• Subjects: Robotics (cs.RO)
• Arxiv link: https://arxiv.org/abs/2202.11140
• Pdf link: https://arxiv.org/pdf/2202.11140
• Abstract When humans control robot arms these robots often need to infer the human's desired task. Prior research on assistive teleoperation and shared autonomy explores how robots can determine the desired task based on the human's joystick inputs. In order to perform this inference the robot relies on an internal mapping between joystick inputs and discrete tasks: e.g., pressing the joystick left indicates that the human wants a plate, while pressing the joystick right indicates a cup. This approach works well \textit{after} the human understands how the robot interprets their inputs -- but inexperienced users still have to learn these mappings through trial and error! Here we recognize that the robot's mapping between tasks and inputs is a \textit{convention}. There are multiple, equally efficient conventions that the robot could use: rather than passively waiting for the human, we introduce a shared autonomy approach where the robot \textit{actively reveals} its chosen convention. Across repeated interactions the robot intervenes and exaggerates the arm's motion to demonstrate more efficient inputs while also assisting for the current task. We compare this approach to a state-of-the-art baseline -- where users must identify the convention by themselves -- as well as written instructions. Our user study results indicate that modifying the robot's behavior to reveal its convention outperforms the baselines and reduces the amount of time that humans spend controlling the robot. See videos of our user study here: \url{https://youtu.be/jROTVOp469I}

  Direct Serendipity and Mixed Finite Elements on Convex Polygons

• Authors: Todd Arbogast, Chuning Wang
• Subjects: Numerical Analysis (math.NA)
• Arxiv link: https://arxiv.org/abs/2202.11229
• Pdf link: https://arxiv.org/pdf/2202.11229
• Abstract We construct new families of direct serendipity and direct mixed finite elements on general planer convex polygons that are $H^1$ and $H(div)$ conforming, respectively, and possess optimal order of accuracy for any order. They have a minimal number of degrees of freedom subject to the conformity and accuracy constraints. The name arises because the shape functions are defined directly on the physical elements, i.e., without using a mapping from a reference element. The finite element shape functions are defined to be the full spaces of scalar or vector polynomials plus a space of supplemental functions. The direct serendipity elements are the precursors of the direct mixed elements in a de Rham complex. The convergence properties of the finite elements are shown under a regularity assumption on the shapes of the polygons in the mesh, as well as some mild restrictions on the choices one can make in the construction of the supplemental functions. Numerical experiments on various meshes exhibit the performance of these new families of finite elements.

  Circuit and System Technologies for Energy-Efficient Edge Robotics

• Authors: Zishen Wan, Ashwin Sanjay Lele, Arijit Raychowdhury
• Subjects: Hardware Architecture (cs.AR); Robotics (cs.RO)
• Arxiv link: https://arxiv.org/abs/2202.11237
• Pdf link: https://arxiv.org/pdf/2202.11237
• Abstract As we march towards the age of ubiquitous intelligence, we note that AI and intelligence are progressively moving from the cloud to the edge. The success of Edge-AI is pivoted on innovative circuits and hardware that can enable inference and limited learning in resource-constrained edge autonomous systems. This paper introduces a series of ultra-low-power accelerator and system designs on enabling the intelligence in edge robotic platforms, including reinforcement learning neuromorphic control, swarm intelligence, and simultaneous mapping and localization. We put an emphasis on the impact of the mixed-signal circuit, neuro-inspired computing system, benchmarking and software infrastructure, as well as algorithm-hardware co-design to realize the most energy-efficient Edge-AI ASICs for the next-generation intelligent and autonomous systems.

  Scalable Query Answering under Uncertainty to Neuroscientific Ontological Knowledge: The NeuroLang Approach

• Authors: Gaston Zanitti (PARIETAL), Yamil Soto (UNS), Valentin Iovene (PARIETAL), Maria Vanina Martinez, Ricardo Rodriguez, Gerardo Simari (UNS), Demian Wassermann (PARIETAL)
• Subjects: Artificial Intelligence (cs.AI)
• Arxiv link: https://arxiv.org/abs/2202.11333
• Pdf link: https://arxiv.org/pdf/2202.11333
• Abstract Researchers in neuroscience have a growing number of datasets available to study the brain, which is made possible by recent technological advances. Given the extent to which the brain has been studied, there is also available ontological knowledge encoding the current state of the art regarding its different areas, activation patterns, key words associated with studies, etc. Furthermore, there is an inherent uncertainty associated with brain scans arising from the mapping between voxels -- 3D pixels -- and actual points in different individual brains. Unfortunately, there is currently no unifying framework for accessing such collections of rich heterogeneous data under uncertainty, making it necessary for researchers to rely on ad hoc tools. In particular, one major weakness of current tools that attempt to address this kind of task is that only very limited propositional query languages have been developed. In this paper, we present NeuroLang, an ontology language with existential rules, probabilistic uncertainty, and built-in mechanisms to guarantee tractable query answering over very large datasets. After presenting the language and its general query answering architecture, we discuss real-world use cases showing how NeuroLang can be applied to practical scenarios for which current tools are inadequate.

  DL-SLOT: Dynamic Lidar SLAM and Object Tracking Based On Graph Optimization

• Authors: Xuebo Tian, Junqiao Zhao, Chen Ye
• Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI)
• Arxiv link: https://arxiv.org/abs/2202.11431
• Pdf link: https://arxiv.org/pdf/2202.11431
• Abstract Ego-pose estimation and dynamic object tracking are two key issues in an autonomous driving system. Two assumptions are often made for them, i.e. the static world assumption of simultaneous localization and mapping (SLAM) and the exact ego-pose assumption of object tracking, respectively. However, these assumptions are difficult to hold in highly dynamic road scenarios where SLAM and object tracking become correlated and mutually beneficial. In this paper, DL-SLOT, a dynamic Lidar SLAM and object tracking method is proposed. This method integrates the state estimations of both the ego vehicle and the static and dynamic objects in the environment into a unified optimization framework, to realize SLAM and object tracking (SLOT) simultaneously. Firstly, we implement object detection to remove all the points that belong to potential dynamic objects. Then, LiDAR odometry is conducted using the filtered point cloud. At the same time, detected objects are associated with the history object trajectories based on the time-series information in a sliding window. The states of the static and dynamic objects and ego vehicle in the sliding window are integrated into a unified local optimization framework. We perform SLAM and object tracking simultaneously in this framework, which significantly improves the robustness and accuracy of SLAM in highly dynamic road scenarios and the accuracy of objects' states estimation. Experiments on public datasets have shown that our method achieves better accuracy than A-LOAM.

  Paying U-Attention to Textures: Multi-Stage Hourglass Vision Transformer for Universal Texture Synthesis

• Authors: Shouchang Guo, Valentin Deschaintre, Douglas Noll, Arthur Roullier
• Subjects: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Image and Video Processing (eess.IV)
• Arxiv link: https://arxiv.org/abs/2202.11703
• Pdf link: https://arxiv.org/pdf/2202.11703
• Abstract We present a novel U-Attention vision Transformer for universal texture synthesis. We exploit the natural long-range dependencies enabled by the attention mechanism to allow our approach to synthesize diverse textures while preserving their structures in a single inference. We propose a multi-stage hourglass backbone that attends to the global structure and performs patch mapping at varying scales in a coarse-to-fine-to-coarse stream. Further completed by skip connection and convolution designs that propagate and fuse information at different scales, our U-Attention architecture unifies attention to microstructures, mesostructures and macrostructures, and progressively refines synthesis results at successive stages. We show that our method achieves stronger 2$\times$ synthesis than previous work on both stochastic and structured textures while generalizing to unseen textures without fine-tuning. Ablation studies demonstrate the effectiveness of each component of our architecture.

  Keyword: localization

  Circuit and System Technologies for Energy-Efficient Edge Robotics

• Authors: Zishen Wan, Ashwin Sanjay Lele, Arijit Raychowdhury
• Subjects: Hardware Architecture (cs.AR); Robotics (cs.RO)
• Arxiv link: https://arxiv.org/abs/2202.11237
• Pdf link: https://arxiv.org/pdf/2202.11237
• Abstract As we march towards the age of ubiquitous intelligence, we note that AI and intelligence are progressively moving from the cloud to the edge. The success of Edge-AI is pivoted on innovative circuits and hardware that can enable inference and limited learning in resource-constrained edge autonomous systems. This paper introduces a series of ultra-low-power accelerator and system designs on enabling the intelligence in edge robotic platforms, including reinforcement learning neuromorphic control, swarm intelligence, and simultaneous mapping and localization. We put an emphasis on the impact of the mixed-signal circuit, neuro-inspired computing system, benchmarking and software infrastructure, as well as algorithm-hardware co-design to realize the most energy-efficient Edge-AI ASICs for the next-generation intelligent and autonomous systems.

  Localizing Small Apples in Complex Apple Orchard Environments

• Authors: Christian Wilms, Robert Johanson, Simone Frintrop
• Subjects: Computer Vision and Pattern Recognition (cs.CV)
• Arxiv link: https://arxiv.org/abs/2202.11372
• Pdf link: https://arxiv.org/pdf/2202.11372
• Abstract The localization of fruits is an essential first step in automated agricultural pipelines for yield estimation or fruit picking. One example of this is the localization of apples in images of entire apple trees. Since the apples are very small objects in such scenarios, we tackle this problem by adapting the object proposal generation system AttentionMask that focuses on small objects. We adapt AttentionMask by either adding a new module for very small apples or integrating it into a tiling framework. Both approaches clearly outperform standard object proposal generation systems on the MinneApple dataset covering complex apple orchard environments. Our evaluation further analyses the improvement w.r.t. the apple sizes and shows the different characteristics of our two approaches.

  DL-SLOT: Dynamic Lidar SLAM and Object Tracking Based On Graph Optimization

• Authors: Xuebo Tian, Junqiao Zhao, Chen Ye
• Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI)
• Arxiv link: https://arxiv.org/abs/2202.11431
• Pdf link: https://arxiv.org/pdf/2202.11431
• Abstract Ego-pose estimation and dynamic object tracking are two key issues in an autonomous driving system. Two assumptions are often made for them, i.e. the static world assumption of simultaneous localization and mapping (SLAM) and the exact ego-pose assumption of object tracking, respectively. However, these assumptions are difficult to hold in highly dynamic road scenarios where SLAM and object tracking become correlated and mutually beneficial. In this paper, DL-SLOT, a dynamic Lidar SLAM and object tracking method is proposed. This method integrates the state estimations of both the ego vehicle and the static and dynamic objects in the environment into a unified optimization framework, to realize SLAM and object tracking (SLOT) simultaneously. Firstly, we implement object detection to remove all the points that belong to potential dynamic objects. Then, LiDAR odometry is conducted using the filtered point cloud. At the same time, detected objects are associated with the history object trajectories based on the time-series information in a sliding window. The states of the static and dynamic objects and ego vehicle in the sliding window are integrated into a unified local optimization framework. We perform SLAM and object tracking simultaneously in this framework, which significantly improves the robustness and accuracy of SLAM in highly dynamic road scenarios and the accuracy of objects' states estimation. Experiments on public datasets have shown that our method achieves better accuracy than A-LOAM.

  Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors

• Authors: Paul Bergmann, David Sattlegger
• Subjects: Computer Vision and Pattern Recognition (cs.CV)
• Arxiv link: https://arxiv.org/abs/2202.11660
• Pdf link: https://arxiv.org/pdf/2202.11660
• Abstract We present a new method for the unsupervised detection of geometric anomalies in high-resolution 3D point clouds. In particular, we propose an adaptation of the established student-teacher anomaly detection framework to three dimensions. A student network is trained to match the output of a pretrained teacher network on anomaly-free point clouds. When applied to test data, regression errors between the teacher and the student allow reliable localization of anomalous structures. To construct an expressive teacher network that extracts dense local geometric descriptors, we introduce a novel self-supervised pretraining strategy. The teacher is trained by reconstructing local receptive fields and does not require annotations. Extensive experiments on the comprehensive MVTec 3D Anomaly Detection dataset highlight the effectiveness of our approach, which outperforms the next-best method by a large margin. Ablation studies show that our approach meets the requirements of practical applications regarding performance, runtime, and memory consumption.