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Edge Enhanced Implicit Orientation learning With Geometric Prior for 6D Pose Estimation

IEEE ROBOTICS AND AUTOMATION LETTERS 2020年第3期5卷 4931-4938页

作者： Wen, Yilin Pan, Hao Yang, Lei Wang, Wenping Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Microsoft Res Asia Beijing 100080 Peoples R China

Estimating 6D poses of rigid objects from RGB images is an important but challenging task. This is especially true for textureless objects with strong symmetry, since they have only sparse visual features to be leveraged for the task and their symmetry leads to pose ambiguity. The implicit encoding of orientations learned by autoencoders [31], [32] has demonstrated its effectiveness in handling such objects without requiring explicit pose labeling. In this letter, we further improve this methodology with two key technical contributions. First, we use edge cues to complement the color images with more discriminative features and reduce the domain gap between the real images for testing and the synthetic ones for training. Second, we enhance the regularity of the implicitly learned pose representations by a self-supervision scheme to enforce the geometric prior that the latent representations of two images presenting nearby rotations should be close too. Our approach achieves the state-of-the-art performance on the T-LESS benchmark in the RGB domain;its evaluation on the LINEMOD dataset also outperforms other synthetically trained approaches. Extensive ablation tests demonstrate the improvements enabled by our technical designs. Our code is publicly available for research use.

关键词： deep learning for visual perception representation learning

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MOLTR: Multiple Object Localization, Tracking and Reconstruction From Monocular RGB Videos

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IEEE ROBOTICS AND AUTOMATION LETTERS 2021年第2期6卷 3341-3348页

作者： Li, Kejie Rezatofighi, Hamid Reid, Ian Univ Adelaide Sch Comp Sci Adelaide SA 5005 Australia Australian Ctr Robot Vis Adelaide SA 5005 Australia Monash Univ Fac Informat Technol Dept Data Sci & AI Melbourne Vic 3800 Australia

Semantic aware reconstruction is more advantageous than geometric-only reconstruction for future robotic and AR/VR applications because it represents not only where things are, but also what things are. Object-centric mapping is a task to build an object-level reconstruction where objects are separate and meaningful entities that convey both geometry and semantic information. In this letter, we present MOLTR, a solution to object-centric mapping using only monocular image sequences and camera poses. It is able to localize, track and reconstruct multiple rigid objects in an online fashion when a RGB camera captures a video of the surrounding. Given a new RGB frame, MOLTR firstly applies a monocular 3D detector to localize objects of interest and extract their shape codes that represent the object shape in a learnt embedding space. Detections are then merged to existing objects in the map after data association. Motion state (i.e., kinematics and the motion status) of each object is tracked by a multiple model Bayesian filter and object shape is progressively refined by fusing multiple shape code. We evaluate localization, tracking and reconstruction on benchmarking datasets for indoor and outdoor scenes, and show superior performance over previous approaches.

关键词： Mapping deep learning for visual perception recognition

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Simultaneously learning Corrections and Error Models for Geometry-Based visual Odometry Methods

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IEEE ROBOTICS AND AUTOMATION LETTERS 2020年第4期5卷 6536-6543页

作者： De Maio, Andrea Lacroix, Simon Univ Toulouse LAAS CNRS F-31031 Toulouse France

This letter fosters the idea that deep learning methods can be used to complement classical visual odometry pipelines to improve their accuracy and to associate uncertainty models to their estimations. We show that the biases inherent to the visual odometry process can be faithfully learned and compensated for, and that a learning architecture associated with a probabilistic loss function can jointly estimate a full covariance matrix of the residual errors, defining an error model capturing the heteroscedasticity of the process. Experiments on autonomous driving image sequences assess the possibility to concurrently improve visual odometry and estimate an error associated with its outputs.

关键词： Covariance matrices Uncertainty visual odometry Estimation Matrix decomposition Probabilistic logic Motion estimation deep learning for visual perception visual-based navigation localization

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Collaborative Multi-Object Tracking With Conformal Uncertainty Propagation

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第4期9卷 3323-3330页

作者： Su, Sanbao Han, Songyang Li, Yiming Zhang, Zhili Feng, Chen Ding, Caiwen Miao, Fei Univ Connecticut Dept Comp Sci & Engn Storrs CT 06268 USA Univ Connecticut Storrs CT 06268 USA NYU Tandon Sch Engn Brooklyn NY 11201 USA

Object detection and multiple object tracking (MOT) are essential components of self-driving systems. Accurate detection and uncertainty quantification are both critical for onboard modules, such as perception, prediction, and planning, to improve the safety and robustness of autonomous vehicles. Collaborative object detection (COD) has been proposed to improve detection accuracy and reduce uncertainty by leveraging the viewpoints of multiple agents. However, little attention has been paid to how to leverage the uncertainty quantification from COD to enhance MOT performance. In this letter, as the first attempt to address this challenge, we design an uncertainty propagation framework called MOT-CUP. Our framework first quantifies the uncertainty of COD through direct modeling and conformal prediction, and propagates this uncertainty information into the motion prediction and association steps. MOT-CUP is designed to work with different collaborative object detectors and baseline MOT algorithms. We evaluate MOT-CUP on V2X-Sim, a comprehensive collaborative perception dataset, and demonstrate a 2% improvement in accuracy and a 2.67 x reduction in uncertainty compared to the baselines, e.g. SORT and ByteTrack. In scenarios characterized by high occlusion levels, our MOT-CUP demonstrates a noteworthy 4.01% improvement in accuracy. MOT-CUP demonstrates the importance of uncertainty quantification in both COD and MOT, and provides the first attempt to improve the accuracy and reduce the uncertainty in MOT based on COD through uncertainty propagation.

关键词： Uncertainty Object detection Collaboration Kalman filters Prediction algorithms Predictive models Estimation Computer vision for transportation deep learning for visual perception multi-robot systems visual tracking

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learning Rearrangement Manipulation via Scene Prediction in Point Cloud

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第12期9卷 11090-11097页

作者： Ma, Anji Duan, Xingguang Beijing Inst Technol Sch Mechatron Engn Beijing 100081 Peoples R China

Predicting scene evolution conditioned on robotic actions is a vital technique in modeling robot manipulations. Previous studies have primarily focused on learning spatiotemporally continuous actions like Cartesian displacements, thus applying them to planar-pushing tasks. In this letter, we propose a scene prediction model that learns higher-level robot actions, such as grasping and pick-and-place, and is applicable to planning such actions in rearrangement manipulations. The model takes partially observed point clouds (e.g., from a single camera) and robot pick-and-place actions to predict point clouds of future scenes. The model directly learns scene prediction using point cloud observation and representation, without requiring prior knowledge of object properties like cad models, pose, or instance segmentation. We train the model only on a synthetic dataset acquired entirely automatically with minimal human intervention. Our experiments validate that our model can substantially learn robot grasping and pick-and-place actions and show that, when integrated into a sample-based planning framework, predicting scenes in point clouds outperforms the image-based baseline in grasping and rearrangement manipulation. Moreover, results show that our method can be directly transferred to real-world environments without fine-tuning and show promising performance on a collection of 18 household objects.

关键词： deep learning in grasping and manipulation manipulation planning deep learning for visual perception deep learning in grasping and manipulation manipulation planning

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learning-To-Rank Approach for Identifying Everyday Objects Using a Physical-World Search Engine

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第3期9卷 2088-2095页

作者： Kaneda, Kanta Nagashima, Shunya Korekata, Ryosuke Kambara, Motonari Sugiura, Komei Keio Univ Yokohama 2238522 Japan

Domestic service robots offer a solution to the increasing demand for daily care and support. A human-in-the-loop approach that combines automation and operator intervention is considered to be a realistic approach to their use in society. Therefore, we focus on the task of retrieving target objects from open-vocabulary user instructions in a human-in-the-loop setting, which we define as the learning-to-rank physical objects (LTRPO) task. For example, given the instruction "Please go to the dining room which has a round table. Pick up the bottle on it," the model is required to output a ranked list of target objects that the operator/user can select. In this letter, we propose MultiRankIt, which is a novel approach for the LTRPO task. MultiRankIt introduces the Crossmodal Noun Phrase Encoder to model the relationship between phrases that contain referring expressions and the target bounding box, and the Crossmodal Region Feature Encoder to model the relationship between the target object and multiple images of its surrounding contextual environment. Additionally, we built a new dataset for the LTRPO task that consists of instructions with complex referring expressions accompanied by real indoor environmental images that feature various target objects. We validated our model on the dataset and it outperformed the baseline method in terms of the mean reciprocal rank and recall@k. Furthermore, we conducted physical experiments in a setting where a domestic service robot retrieved everyday objects in a standardized domestic environment, based on users' instruction in a human-in-the-loop setting. The experimental results demonstrate that the success rate for object retrieval achieved 80%.

关键词： deep learning methods learning categories and concepts deep learning for visual perception

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DiffMap: Enhancing Map Segmentation With Map Prior Using Diffusion Model

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第11期9卷 9836-9843页

作者： Jia, Peijin Wen, Tuopu Luo, Ziang Yang, Mengmeng Jiang, Kun Liu, Ziyuan Tang, Xuewei Lei, Zhiquan Cui, Le Zhang, Bo Sheng, Kehua Yang, Diange Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China DiDi Chuxing Tianjin 300450 Peoples R China

Constructing high-definition (HD) maps is a crucial requirement for enabling autonomous driving. In recent years, several map segmentation algorithms have been developed to address this need, leveraging advancements in Bird's-Eye View (BEV) perception. However, existing models still encounter challenges in producing realistic and consistent semantic map layouts. A prominent issue is the limited utilization of structured priors inherent in map segmentation masks. In light of this, we propose DiffMap, a novel approach specifically designed to model the structured priors of map segmentation masks using latent diffusion model. By incorporating this technique, the performance of existing semantic segmentation methods can be significantly enhanced and certain structural errors present in the segmentation outputs can be effectively rectified. Notably, the proposed module can be seamlessly integrated into any map segmentation model, thereby augmenting its capability to accurately delineate semantic information. Furthermore, through extensive visualization analysis, our model demonstrates superior proficiency in generating results that more accurately reflect real-world map layouts, further validating its efficacy in improving the quality of the generated maps.

关键词： Semantics Diffusion models Noise reduction Predictive models Laser radar Standards Noise Autonomous vehicle navigation deep learning for visual perception sensor fusion

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Delta Descriptors: Change-Based Place Representation for Robust visual Localization

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IEEE ROBOTICS AND AUTOMATION LETTERS 2020年第4期5卷 5120-5127页

作者： Garg, Sourav Harwood, Ben Anand, Gaurangi Milford, Michael Queensland Univ Technol Sch Elect Engn & Robot Brisbane Qld Australia Australian Ctr Robot Vis Brisbane Qld 4001 Australia QUT Ctr Robot Brisbane Qld 4001 Australia Monash Univ Dept Elect & Comp Syst Engn Clayton Vic 3800 Australia Queensland Univ Technol Sch Comp Sci Brisbane Qld 4001 Australia

visual place recognition is challenging because there are so many factors that can cause the appearance of a place to change, from day-night cycles to seasonal change to atmospheric conditions. In recent years a large range of approaches have been developed to address this challenge including deep-learnt image descriptors, domain translation, and sequential filtering, all with shortcomings including generality and velocity-sensitivity. In this letter we propose a novel descriptor derived from tracking changes in any learned global descriptor over time, dubbed Delta Descriptors. Delta Descriptors mitigate the offsets induced in the original descriptor matching space in an unsupervised manner by considering temporal differences across places observed along a route. Like all other approaches, Delta Descriptors have a shortcoming - volatility on a frame to frame basis - which can be overcome by combining them with sequential filtering methods. Using two benchmark datasets, we first demonstrate the high performance of Delta Descriptors in isolation, before showing new state-of-the-art performance when combined with sequence-based matching. We also present results demonstrating the approach working with four different underlying descriptor types, and two other beneficial properties of Delta Descriptors in comparison to existing techniques: their increased inherent robustness to variations in camera motion and a reduced rate of performance degradation as dimensional reduction is applied. Source code is made available at https://***/oravus/DeltaDescriptors.

关键词： Localization deep learning for visual perception recognition computer vision for automation autonomous vehicle navigation

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DenseLiDAR: A Real-Time Pseudo Dense Depth Guided Depth Completion Network

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IEEE ROBOTICS AND AUTOMATION LETTERS 2021年第2期6卷 1808-1815页

作者： Gu, Jiaqi Xiang, Zhiyu Ye, Yuwen Wang, Lingxuan Zhejiang Univ Coll Informat Sci & Eletron Engn Hangzhou 310027 Peoples R China Zhejiang Univ Zhejiang Prov Key Lab Informat Proc Commun & Netw Hangzhou 310027 Peoples R China

Depth Completion can produce a dense depth map from a sparse input and provide a more complete 3D description of the environment. Despite great progress made in depth completion, the sparsity of the input and low density of the ground truth still make this problem challenging. In this work, we propose DenseLiDAR, a novel real-time pseudo-depth guided depth completion neural network. We exploit dense pseudo-depth map obtained from simple morphological operations to guide the network in three aspects: (1) Constructing a residual structure for the output;(2) Rectifying the sparse input data;(3) Providing dense structural loss for training the network. Thanks to these novel designs, higher performance of the output could be achieved. In addition, two new metrics for better evaluating the quality of the predicted depth map are also presented. Extensive experiments on KITTI depth completion benchmark suggest that our model is able to achieve the state-of-the-art performance at the highest frame rate of 50 Hz. The predicted dense depth is further evaluated by several downstream robotic perception or positioning tasks. For the task of 3D object detection, 3(similar to)5 percent performance gains on small objects categories are achieved on KITTI 3D object detection dataset. For RGB-D SLAM, higher accuracy on vehicle's trajectory is also obtained in KITTI Odometry dataset. These promising results not only verify the high quality of our depth prediction, but also demonstrate the potential of improving the related downstream tasks by using depth completion results.

关键词： deep learning for visual perception RGB-D perception recognition

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SE3ET: SE(3)-Equivariant Transformer for Low-Overlap Point Cloud Registration

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第11期9卷 9526-9533页

作者： Lin, Chien Erh Zhu, Minghan Ghaffari, Maani Univ Michigan Ann Arbor MI 48109 USA

Partial point cloud registration is a challenging problem in robotics, especially when the robot undergoes a large transformation, causing a significant initial pose error and a low overlap between measurements. This letter proposes exploiting equivariant learning from 3D point clouds to improve registration robustness. We propose SE3ET, an SE(3)-equivariant registration framework that employs equivariant point convolution and equivariant transformer designs to learn expressive and robust geometric features. We tested the proposed registration method on indoor and outdoor benchmarks where the point clouds are under arbitrary transformations and low overlapping ratios. We also provide generalization tests and run-time performance.

关键词： Point cloud compression Transformers Feature extraction Three-dimensional displays Representation learning Convolution Decoding deep learning for visual perception localization deep learning methods

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