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Automatic Labeling to Generate Training Data for Online LiDAR-Based Moving Object segmentation

IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第3期7卷 6107-6114页

作者： Chen, Xieyuanli Mersch, Benedikt Nunes, Lucas Marcuzzi, Rodrigo Vizzo, Ignacio Behley, Jens Stachniss, Cyrill Univ Bonn D-53115 Bonn Germany Univ Oxford Dept Engn Sci Oxford OX1 2JD England

Understanding the scene is key for autonomously navigating vehicles, and the ability to segment the surroundings online into moving and non-moving objects is a central ingredient of this task. Often, deep learning-based methods are used to perform moving object segmentation (MOS). The performance of these networks, however, strongly depends on the diversity and amount of labeled training data-information that may be costly to obtain. In this letter, we propose an automatic data labeling pipeline for 3D LiDAR data to save the extensive manual labeling effort and to improve the performance of existing learning-based MOS systems by automatically annotation training data. Our proposed approach achieves this by processing the data offline in batches, i.e., it is not designed to run online on a vehicle. It labels the actually moving objects such as driving cars and pedestrians as moving. In contrast, the non-moving objects, e.g., parked cars, lamps, roads, or buildings, are labeled as static. We show that this approach allows us to label LiDAR data highly effectively and compare our results to those of other label generation methods. We also train a deep neural network with our automatically generated labels and achieve comparable performance to the one trained with manual labels on the same data-and an even better performance when using additional datasets with labels generated by our approach. Furthermore, we evaluate our method on multiple datasets using different sensors, and our experiments indicate that our method can generate labels in different outdoor environments.

关键词： Deep learning methods object detection segmentation and categorization semantic scene understanding

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Perceiving the Invisible: Proposal-Free Amodal Panoptic segmentation

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第4期7卷 9302-9309页

作者： Mohan, Rohit Valada, Abhinav Univ Freiburg Dept Comp Sci D-79085 Freiburg Germany

Amodal panoptic segmentation aims to connect the perception of the world to its cognitive understanding. It entails simultaneously predicting the semantic labels of visible scene regions and the entire shape of traffic participant instances, including regions that may be occluded. In this work, we formulate a proposal-free framework that tackles this task as a multi-label and multi-class problem by first assigning the amodal masks to different layers according to their relative occlusion order and then employing amodal instance regression on each layer independently while learning background semantics. We propose the PAPS architecture that incorporates a shared backbone and an asymmetrical dual-decoder consisting of several modules to facilitate within-scale and cross-scale feature aggregations, bilateral feature propagation between decoders, and integration of global instance-level and local pixel-level occlusion reasoning. Further, we propose the amodal mask refiner that resolves the ambiguity in complex occlusion scenarios by explicitly leveraging the embedding of unoccluded instance masks. Extensive evaluation on the BDD100K-APS and KITTI-360-APS datasets demonstrate that our approach set the new state-of-the-art on both benchmarks.

关键词： Deep learning for visual perception object detection segmentation and categorization semantic scene understanding

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Sem-Aug: Improving Camera-LiDAR Feature Fusion With Semantic Augmentation for 3D Vehicle Detection

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第4期7卷 9358-9365页

作者： Zhao, Lin Wang, Meiling Yue, Yufeng Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

Camera-LiDAR fusion provides precise distance measurements and fine-grained textures, making it a promising option for 3D vehicle detection in autonomous driving scenarios. Previous camera-LiDAR based 3D vehicle detection approaches mainly focused on employing image-based pre-trained models to fetch semantic features. However, these methods may perform inferior to the LiDAR-based ones when lacking semantic segmentation labels in autonomous driving tasks. Motivated by this observation, we propose a novel semantic augmentation method, namely Sem-Aug, to guide high-confidence camera-LiDAR fusion feature generation and boost the performance of multimodal 3D vehicle detection. The key novelty of semantic augmentation lies in the 2D segmentation mask auto-labeling, which provides supervision for semantic segmentation sub-network to mitigate the poor generalization performance of camera-LiDAR fusion. Using semantic-augmentation-guided camera-LiDAR fusion features, Sem-Aug achieves remarkable performance on the representative autonomous driving KITTI dataset compared to both the LiDAR-based baseline and previous multimodal 3D vehicle detectors. Qualitative and quantitative experiments demonstrate that Sem-Aug provides significant improvements in challenging Hard detection scenarios caused by occlusion and truncation.

关键词： Computer vision for transportation intelligent transportation systems object detection segmentation and categorization

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Keypoints-Based Deep Feature Fusion for Cooperative Vehicle Detection of Autonomous Driving

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第2期7卷 3054-3061页

作者： Yuan, Yunshuang Cheng, Hao Sester, Monika Leibniz Univ Hannover Inst Cartog & Geoinformat D-30167 Hannover Germany

Sharing collective perception messages (CPM) between vehicles is investigated to decrease occlusions so as to improve the perception accuracy and safety of autonomous driving. However, highly accurate data sharing and low communication overhead is a big challenge for collective perception, especially when real-time communication is required among connected and automated vehicles. In this letter, we propose an efficient and effective keypoints-based deep feature fusion framework built on the 3D object detector PV-RCNN, called Fusion PV-RCNN (FPV-RCNN for short), for collective perception. We introduce a high-performance bounding box proposal matching module and a keypoints selection strategy to compress the CPM size and solve the multi-vehicle data fusion problem. Besides, we also propose an effective localization error correction module based on the maximum consensus principle to increase the robustness of the data fusion. Compared to a bird's-eye view (BEV) keypoints feature fusion, FPV-RCNN achieves improved detection accuracy by about 9% at a high evaluation criterion (IoU 0.7) on the synthetic dataset COMAP dedicated to collective perception. In addition, its performance is comparable to two raw data fusion baselines that have no data loss in sharing. Moreover, our method also significantly decreases the CPM size to less than 03 KB, and is thus about SO times smaller than the BEV feature map sharing used in previous works. Even with further decreased CPM feature channels, i. e., from 128 to 32, the detection performance does not show apparent drops. The code of our method is available at https://***/YuanYunshuang/FPV_RCNN.

关键词： Sensor fusion sensor networks object detection segmentation and categorization

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Embodied Active Domain Adaptation for Semantic segmentation via Informative Path Planning

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第4期7卷 8691-8698页

作者： Zurbruegg, Rene Blum, Hermann Cadena, Cesar Siegwart, Roland Schmid, Lukas Swiss Fed Inst Technol Autonomous Syst Lab CH-8090 Zurich Switzerland

This work presents an embodied agent that can adapt its semantic segmentation network to new indoor environments in a fully autonomous way. Because semantic segmentation networks fail to generalize well to unseen environments, the agent collects images of the new environment which are then used for self-supervised domain adaptation. We formulate this as an informative path planning problem, and present a novel information gain that leverages uncertainty extracted from the semantic model to safely collect relevant data. As domain adaptation progresses, these uncertainties change over time and the rapid learning feedback of our system drives the agent to collect different data. Experiments show that our method adapts to new environments faster and with higher final performance compared to an exploration objective, and can successfully be deployed to real-world environments on physical robots.

关键词： Perception-action coupling integrated planning and learning object detection segmentation and categorization

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ATF-3D: Semi-Supervised 3D Object Detection With Adaptive Thresholds Filtering Based on Confidence and Distance

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第4期7卷 10573-10580页

作者： Zhang, Zehan Ji, Yang Cui, Wei Wang, Yulong Li, Hao Zhao, Xian Li, Duo Tang, Sanli Yang, Ming Tan, Wenming Pu, Shiliang Hangzhou Hikvis Digital Technol Co Ltd Hikvis Res Inst Hangzhou 310052 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

Performance of current point cloud-based outdoor 3D object detection relies heavily on large-scale high-quality 3D annotations. However, such annotations are usually expensive to collect and outdoor scenes easily accumulate massive unlabeled data containing rich scenes. Semi-supervised learning is a effective alternative to utilize both labeled and unlabeled data, but remains unexplored in outdoor 3D object detection. Inspired by indoor semi-supervised 3D detection methods, SESS and 3DIoUMatch, we propose ATF-3D, a semi-supervised 3D object detection framework for outdoor scenes. Specifically, we design a simple yet effective adaptive thresholds search method based on distances and categories for obtaining high-quality pseudo labels. Concurrently, we propose an iterative training mechanism with pseudo-label training and self-ensembling learning to combine the advantages of both schemes. Furthermore, we adopt point cloud data augmentations in the self-ensembling learning stage to further improve the performance. Our ATF-3D ranks first among all single-model methods in the ONCE benchmark. Results on both ONCE and Waymo datasets demonstrate substatial improvements over the supervised baseline.

关键词： Object detection segmentation and categorization autonomous agents localization semi-supervised learning

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OSSID: Online Self-Supervised Instance Detection by (And For) Pose Estimation

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第2期7卷 3022-3029页

作者： Gu, Qiao Okorn, Brian Held, David Univ Toronto Dept Comp Sci Toronto ON M5S 1A1 Canada Carnegie Mellon Univ Robot Inst Pittsburgh PA 15214 USA

Real-time object pose estimation is necessary for many robot manipulation algorithms. However, state-of-the-art methods for object pose estimation are trained for a specific set of objects;these methods thus need to be retrained to estimate the pose of each new object, often requiring tens of GPU-days of training for optimal performance. In this letter, we propose the OSSID framework, leveraging a slow zero-shot pose estimator to self-supervise the training of a fast detection algorithm. This fast detector can then be used to filter the input to the pose estimator, drastically improving its inference speed. We show that this self-supervised training exceeds the performance of existing zero-shot detection methods on two widely used object pose estimation and detection datasets, without requiring any human annotations. Further, we show that the resulting method for pose estimation has a significantly faster inference speed, due to the ability to filter out large parts of the image. Thus, our method for self-supervised online learning of a detector (trained using pseudo-labels from a slow pose estimator) leads to accurate pose estimation at real-time speeds, without requiring human annotations. Supplementary materials and code can be found at https://***/OSSID/

关键词： Object detection segmentation and categorization deep learning for visual perception RGB-D perception

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Self-Supervised Learning for Panoptic segmentation of Multiple Fruit Flower Species

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第4期7卷 12387-12394页

作者： Siddique, Abubakar Tabb, Amy Medeiros, Henry Marquette Univ Dept Elect & Comp Engn Milwaukee WI 53233 USA USDA Kearneysville WV 25430 USA Univ Florida Dept Agr & Biol Engn Gainesville FL 32611 USA

Convolutional neural networks trained using manually generated labels are commonly used for semantic or instance segmentation. In precision agriculture, automated flower detection methods use supervised models and post-processing techniques that may not perform consistently as the appearance of the flowers and the data acquisition conditions vary. We propose a self-supervised learning strategy to enhance the sensitivity of segmentation models to different flower species using automatically generated pseudo-labels. We employ a data augmentation and refinement approach to improve the accuracy of the model predictions. The augmented semantic predictions are then converted to panoptic pseudo-labels to iteratively train a multi-task model. The self-supervised model predictions can be refined with existing post-processing approaches to further improve their accuracy. An evaluation on a multi-species fruit tree flower dataset demonstrates that our method outperforms state-of-the-art models without computationally expensive post-processing steps, providing a new baseline for flower detection applications.

关键词： Agricultural automation incremental learning object detection semantic scene understanding segmentation and categorization

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See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptation

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第3期7卷 7904-7911页

作者： Tsai, Darren Berrio, Julie Stephany Shan, Mao Worrall, Stewart Nebot, Eduardo Univ Sydney Australian Ctr Field Robot ACFR Sydney NSW Australia

Sampling discrepancies between different manufacturers and models of lidar sensors result in inconsistent representations of objects. This leads to performance degradation when 3D detectors trained for one lidar are tested on other types of lidars. Remarkable progress in lidar manufacturing has brought about advances in mechanical, solid-state, and recently, adjustable scan pattern lidars. For the latter, existing works often require fine-tuning the model each time scan patterns are adjusted, which is infeasible. We explicitly deal with the sampling discrepancy by proposing a novel unsupervised multi-target domain adaptation framework, SEE, for transferring the performance of state-of-the-art 3D detectors across both fixed and flexible scan pattern lidars without requiring fine-tuning of models by end-users. Our approach interpolates the underlying geometry and normalises the scan pattern of objects from different lidars before passing them to the detection network. We demonstrate the effectiveness of SEE on public datasets, achieving state-of-the-art results, and additionally provide quantitative results on a novel high-resolution lidar to prove the industry applications of our framework.

关键词： Object detection segmentation and categorization deep learning for visual perception recognition

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FSNet: A Failure Detection Framework for Semantic segmentation

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第2期7卷 3030-3037页

作者： Rahman, Quazi Marufur Sunderhauf, Niko Corke, Peter Dayoub, Feras Queensland Univ Technol Brisbane Qld 4000 Australia

Semantic segmentation is an important task that helps autonomous vehicles understand their surroundings and navigate safely. However, during deployment, even the most mature segmentation models are vulnerable to various external factors that can degrade the segmentation performance with potentially catastrophic consequences for the vehicle and its surroundings. To address this issue, we propose a failure detection framework to identify pixel-level misclassification. We do so by exploiting internal features of the segmentation model and training it simultaneously with a failure detection network. During deployment, the failure detector flags areas in the image where the segmentation model has failed to segment correctly. We evaluate the proposed approach against state-of-the-art methods and achieve 12.30%, 9.46%, and 9.65% performance improvement in the AUPR-Error metric for Cityscapes, BDD100k, and Mapillary semantic segmentation datasets.

关键词： Object detection segmentation and categorization deep learning for visual perception deep learning methods

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