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Efficient Spatial-Temporal Normalization of SAE Representation for Event Camera

IEEE ROBOTICS AND AUTOMATION LETTERS 2020年第3期5卷 4265-4272页

作者： Lin, Shijie Xu, Fang Wang, Xuhong Yang, Wen Yu, Lei Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

Event-based cameras are a new type of vision sensor that can encode spatial-temporal context in a pixel-level event stream. Its appealing properties offer great potential for applications requiring low processing latency and low power consumption. As an effective representation of events, the surface of active event (SAE) has become a favorable choice for corner detection and object classification, among others. These tasks apply normalizations as an essential preprocessing step to extract time-invariant features from SAEs. However, previous normalization methods have some drawbacks, including low efficiency, requiring parameter tuning, etc. These drawbacks largely limit their performances in practical tasks. In this work, we propose a highly efficient normalization method, i.e., chain normalization, to break the limits in the previous state-of-the-art. We leverage the inherent properties of SAE in designing. First, we propose a novel SAE implementation to utilize the characteristics of SAE. Compared with previous works, our method can efficiently capture the spatial and temporal relationships of events and enable robust normalization. Second, we further increase the efficiency by using a novel stacking strategy. We compare our method to the state-of-the-art with extensive experiments, showing high classification accuracy and a significant improvement in runtime performance. We also release the source codes for future distribution and improvement in the community.

关键词： Event-based camera event-based vision recognition object detection segmentation and categorization

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Track to Reconstruct and Reconstruct to Track

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IEEE ROBOTICS AND AUTOMATION LETTERS 2020年第2期5卷 1803-1810页

作者： Luiten, Jonathon Fischer, Tobias Leibe, Bastian Rhein Westfal TH Aachen D-52064 Aachen Germany

Object tracking and 3D reconstruction are often performed together, with tracking used as input for reconstruction. However, the obtained reconstructions also provide useful information for improving tracking. We propose a novel method that closes this loop, first tracking to reconstruct, and then reconstructing to track. Our approach, MOTSFusion (Multi-Object Tracking, segmentation and dynamic object Fusion), exploits the 3D motion extracted from dynamic object reconstructions to track objects through long periods of complete occlusion and to recover missing detections. Our approach first builds up short tracklets using 2D optical flow, and then fuses these into dynamic 3D object reconstructions. The precise 3D object motion of these reconstructions is used to merge tracklets through occlusion into long-term tracks, and to locate objects when detections are missing. On KITTI, our reconstruction-based tracking reduces the number of ID switches of the initial tracklets by more than 50%, and outperforms all previous approaches for both bounding box and segmentation tracking. Code available: https://***/tobiasfshr/MOTSFusion.

关键词： Visual tracking human detection and tracking semantic scene understanding object detection segmentation and categorization computer vision for transportation

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Automated High-Productivity Microinjection System for Adherent Cells

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IEEE ROBOTICS AND AUTOMATION LETTERS 2020年第2期5卷 1167-1174页

作者： Pan, Fei Chen, Shuxun Jiao, Yang Guan, Zhangyan Shakoor, Adnan Sun, Dong City Univ Hong Kong Dept Biomed Engn Hong Kong Peoples R China

Automated microinjection systems for suspension cells have been studied for years. Nevertheless, microinjection systems for adherent cells still suffer from laborious manual operations and low productivity. This paper presents a new automated microinjection system with high productivity for adherent cells. This system enhances productivity through four approaches. First, cells are detected automatically to replace manual selections. Second, the injection paths of detected cells are optimized rapidly to save time. Third, the penetration depth is adjusted adaptively according to the moving plane of the dish holder plate. Finally, constant outflow-based injection is adopted to minimize clogging. The first three approaches aim to improve the injection speed, and the last one aims to extend the usage time of micropipettes. Experiments of massive injections on MC3T3-E1 cells are performed to evaluate cell detection efficiency, injection speed, success rate, and survival rate. Results confirm that the system allows injections of over 1500 cells in one hour without much training and preparation a priori.

关键词： Biological cell manipulation computer vision for other robotic applications object detection segmentation and categorization

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Point Cloud Projective Analysis for Part-Based Grasp Planning

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IEEE ROBOTICS AND AUTOMATION LETTERS 2020年第3期5卷 4695-4702页

作者： Monica, Riccardo Aleotti, Jacopo Univ Parma Dept Engn & Architecture RIMLab I-43124 Parma Italy

This work presents an approach for part-based grasp planning in point clouds. A complete pipeline is proposed that allows a robot manipulator equipped with a range camera to perfirm object detection, categorization, segmentation into meaningful parts, and part-based semantic grasping. A supervised image-space technique is adopted for point cloud segmentation based on projective analysis. Projective analysis generates a set of 2D projections from the input object point cloud, labels each object projection by transferring knowledge from existing labeled images, and then fuses the labels by back-projection on the object point cloud. We introduce an algorithm for point cloud categorization based on 2D projections. We also propose a viewpoint aware algorithm that filters 2D projections according to the scanning path of the robot. Object categorization and segmentation experiments were carried out with both synthetic and real datasets. Results indicate that the proposed approach performs better than a CNN-based method for a training set of limited size. Finally, we show part-based grasping tasks in a real robotic setup.

关键词： Perception for grasping and manipulation object detection segmentation and categorization semantic scene understanding

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Monocular Camera Based Fruit Counting and Mapping With Semantic Data Association

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IEEE ROBOTICS AND AUTOMATION LETTERS 2019年第3期4卷 2296-2303页

作者： Liu, Xu Chen, Steven W. Liu, Chenhao Shivakumar, Shreyas S. Das, Jnaneshwar Taylor, Camillo J. Underwood, James Kumar, Vijay Univ Penn Grasp Lab Philadelphia PA 19104 USA Arizona State Univ Sch Earth & Space Explorat Tempe AZ 85281 USA Univ Sydney Australian Ctr Field Robot Camperdown NSW 2006 Australia

In this letter, we present a cheap, lightweight, and fast fruit counting pipeline. Our pipeline relies only on a monocular camera, and achieves counting performance comparable to a state-of-the-art fruit counting system that utilizes an expensive sensor suite including a monocular camera, LiDAR and GPS/INS on amango dataset. Our pipeline begins with a fruit and tree trunk detection component that uses state-of-the-art convolutional neural networks (CNNs). It then tracks fruits and tree trunks across images, with a Kalman Filter fusing measurements from the CNN detectors and an optical flow estimator. Finally, fruit count and map are estimated by an efficient fruit-as-feature semantic structure from motion algorithm that converts two-dimensional (2-D) tracks of fruits and trunks into 3-D landmarks, and uses these landmarks to identify double counting scenarios. There are many benefits of developing such a low cost and lightweight fruit counting system, including applicability to agriculture in developing countries, where monetary constraints or unstructured environments necessitate cheaper hardware solutions.

关键词： Robotics in agriculture and forestry deep learning in robotics and automation visual tracking mapping object detection segmentation and categorization

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Focal Loss in 3D Object Detection

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IEEE ROBOTICS AND AUTOMATION LETTERS 2019年第2期4卷 1263-1270页

作者： Yun, Peng Tai, Lei Wang, Yuan Liu, Chengju Liu, Ming Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China

3D object detection is still an open problem in autonomous driving scenes. When recognizing and localizing key objects from sparse 3D inputs, autonomous vehicles suffer from a larger continuous searching space and higher fore-background imbalance compared to image-based object detection. In this letter, we aim to solve this fore-background imbalance in 3D object detection. Inspired by the recent use of focal loss in image-based object detection, we extend this hard-mining improvement of binary cross entropy to point-cloud-based object detection and conduct experiments to showits performance based on two different 3D detectors: 3D-FCN and VoxelNet. The evaluation results show up to 11.2AP gains through the focal loss in a wide range of hyperparameters for 3D object detection.

关键词： Deep Learning in Robotics and Automation Object Detection segmentation and categorization Recognition

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MAVNet: An Effective Semantic segmentation Micro-Network for MAV-Based Tasks

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IEEE ROBOTICS AND AUTOMATION LETTERS 2019年第4期4卷 3908-3915页

作者： Nguyen, Ty Shivakumar, Shreyas S. Miller, Ian D. Keller, James Lee, Elijah S. Zhou, Alex Ozaslan, Tolga Loianno, Giuseppe Harwood, Joseph H. Wozencraft, Jennifer Taylor, Camillo J. Kumar, Vijay Univ Penn Grasp Lab Philadelphia PA 19104 USA NYU Tandon Sch Engn Brooklyn NY 11201 USA US Army Corps Engineers Washington DC 20314 USA

Real-time semantic image segmentation on platforms subject to size, weight, and power constraints is a key area of interest for air surveillance and inspection. In this letter, we propose MAVNet: a small, light-weight, deep neural network for real-time semantic segmentation on micro aerial vehicles (MAVs). MAVNet, inspired by ERFNet [E. Romera, J. M. lvarez, L. M. Bergasa, and R. Arroyo, "ErfNet: Efficient residual factorized convnet for real-time semantic segmentation," IEEE Trans. Intell. Transp. Syst., vol. 19, no. 1, pp. 263-272, Jan. 2018.], features 400 times fewer parameters and achieves comparable performance with some reference models in empirical experiments. Additionally, we provide two novel datasets that represent challenges in semantic segmentation for real-time MAV tracking and infrastructure inspection tasks and verify MAVNet on these datasets. Our algorithm and datasets are made publicly available.

关键词： Object detection segmentation and categorization semantic scene understanding aerial systems: perception and autonomy recognition semantic segmentation

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Real-Time Instrument segmentation in Robotic Surgery Using Auxiliary Supervised Deep Adversarial Learning

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IEEE ROBOTICS AND AUTOMATION LETTERS 2019年第2期4卷 2188-2195页

作者： Islam, Mobarakol Atputharuban, Daniel Anojan Ramesh, Ravikiran Ren, Hongliang Natl Univ Singapore NUS Grad Sch Integrat Sci & Engn Singapore 117581 Singapore Natl Univ Singapore Dept Biomed Engn Singapore 117581 Singapore Univ Moratuwa Dept Elect & Telecommun Moratuwa 10400 Sri Lanka Natl Inst Technol Trichy Dept Instrumentat & Control Engn Tiruchirappalli 620015 Tamil Nadu India

Robot-assisted surgery is an emerging technology that has undergone rapid growth with the development of robotics and imaging systems. Innovations in vision, haptics, and accurate movements of robot arms have enabled surgeons to perform precise minimally invasive surgeries. Real-time semantic segmentation of the robotic instruments and tissues is a crucial step in robot-assisted surgery. Accurate and efficient segmentation of the surgical scene not only aids in the identification and tracking of instruments but also provides contextual information about the different tissues and instruments being operated with. For this purpose, we have developed a light-weight cascaded convolutional neural network to segment the surgical instruments from high-resolution videos obtained from a commercial robotic system. We propose a multi-resolution feature fusion module to fuse the feature maps of different dimensions and channels from the auxiliary and main branch. We also introduce a novel way of combining auxiliary loss and adversarial loss to regularize the segmentation model. Auxiliary loss helps the model to learn low-resolution features, and adversarial loss improves the segmentation prediction by learning higher order structural information. The model also consists of a lightweight spatial pyramid pooling unit to aggregate rich contextual information in the intermediate stage. We show that our model surpasses existing algorithms for pixelwise segmentation of surgical instruments in both prediction accuracy and segmentation time of high-resolution videos.

关键词： Deep learning in robotics and automation visual tracking object detection segmentation and categorization

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Multi-View Incremental segmentation of 3-D Point Clouds for Mobile Robots

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IEEE ROBOTICS AND AUTOMATION LETTERS 2019年第2期4卷 1240-1246页

作者： Chen, Jingdao Cho, Yong Kwon Kira, Zsolt Georgia Inst Technol Inst Robot & Intelligent Machines Atlanta GA 30332 USA Georgia Inst Technol Sch Civil & Environm Engn Atlanta GA 30332 USA

Mobile robots need to create high-definition three-dimensional (3-D) maps of the environment for applications such as remote surveillance and infrastructure mapping. Accurate semantic processing of the acquired 3-D point cloud is critical for allowing the robot to obtain a high-level understanding of the surrounding objects and perform context-aware decision making. Existing techniques for point cloud semantic segmentation are mostly applied on a single frame or offline basis, with no way to integrate the segmentation results over time. This letter proposes an online method for mobile robots to incrementally build a semantically rich 3-D point cloud of the environment. The proposed deep neural network, MCPNet, is trained to predict class labels and object instance labels for each point in the scanned point cloud in an incremental fashion. A multi-view context pooling (MCP) operator is used to combine point features obtained from multiple viewpoints to improve the classification accuracy. The proposed architecture was trained and evaluated on ray-traced scans derived from the Stanford 3-D Indoor Spaces dataset. Results show that the proposed approach led to 15% improvement in pointwise accuracy and 7% improvement in normalized mutual information compared to the next best online method, with only a 6% drop in accuracy compared to the PointNet-based offline approach.

关键词： Deep learning in robotics and automation object detection segmentation and categorization semantic scene understanding

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LDLS: 3-D Object segmentation Through Label Diffusion From 2-D Images

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IEEE ROBOTICS AND AUTOMATION LETTERS 2019年第3期4卷 2902-2909页

作者： Wang, Brian H. Chao, Wei-Lun Wang, Yan Hariharan, Bharath Weinberger, Kilian Q. Campbell, Mark Cornell Univ Sibley Sch Mech & Aerosp Engn Ithaca NY 14850 USA Cornell Univ Dept Comp Sci Ithaca NY 14850 USA

Object segmentation in three-dimensional (3-D) point clouds is a critical task for robots capable of 3-D perception. Despite the impressive performance of deep learning-based approaches on object segmentation in 2-D images, deep learning has not been applied nearly as successfully for 3-D point cloud segmentation. Deep networks generally require large amounts of labeled training data, which are readily available for 2-D images but are difficult to produce for 3-D point clouds. In this letter, we present Label Diffusion Lidar segmentation (LDLS), a novel approach for 3-D point cloud segmentation, which leverages 2-D segmentation of an RGB image from an aligned camera to avoid the need for training on annotated 3-D data. We obtain 2-D segmentation predictions by applying Mask-RCNN to the RGB image, and then link this image to a 3-D lidar point cloud by building a graph of connections among 3-D points and 2-D pixels. This graph then directs a semi-supervised label diffusion process, where the 2-D pixels act as source nodes that diffuse object label information through the 3-D point cloud, resulting in a complete 3-D point cloud segmentation. We conduct empirical studies on the KITTI benchmark dataset and on a mobile robot, demonstrating wide applicability and superior performance of LDLS compared with the previous state of the art in 3-D point cloud segmentation, without any need for either 3-D training data or fine tuning of the 2-D image segmentation model.

关键词： Object detection segmentation and categorization RGB-D perception

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