LO-Net: Deep Real-time Lidar Odometry

We present a novel deep convolutional network pipeline, LO-Net, for real-time lidar odometry estimation. Unlike most existing lidar odometry (LO) estimations that go through individually designed feature selection, feature matching, and pose estimation pipeline, LO-Net can be trained in an end-to-end manner. With a new mask-weighted geometric constraint loss, LO-Net can effectively learn feature representation for LO estimation, and can implicitly exploit the sequential dependencies and dynamics in the data. We also design a scan-to-map module, which uses the geometric and semantic information learned in LO-Net, to improve the estimation accuracy. Experiments on benchmark datasets demonstrate that LO-Net outperforms existing learning based approaches and has similar accuracy with the state-of-the-art geometry-based approach, LOAM

Point2Node: Correlation Learning of Dynamic-Node for Point Cloud Feature Modeling

Fully exploring correlation among points in point clouds is essential for their feature modeling. This paper presents a novel end-to-end graph model, named Point2Node, to represent a given point cloud. Point2Node can dynamically explore correlation among all graph nodes from different levels, and adaptively aggregate the learned features. Specifically, first, to fully explore the spatial correlation among points for enhanced feature description, in a high-dimensional node graph, we dynamically integrate the node's correlation with self, local, and non-local nodes. Second, to more effectively integrate learned features, we design a data-aware gate mechanism to self-adaptively aggregate features at the channel level. Extensive experiments on various point cloud benchmarks demonstrate that our method outperforms the state-of-the-art.Comment: AAAI2020(oral

HSC4D: Human-centered 4D Scene Capture in Large-scale Indoor-outdoor Space Using Wearable IMUs and LiDAR

We propose Human-centered 4D Scene Capture (HSC4D) to accurately and efficiently create a dynamic digital world, containing large-scale indoor-outdoor scenes, diverse human motions, and rich interactions between humans and environments. Using only body-mounted IMUs and LiDAR, HSC4D is space-free without any external devices' constraints and map-free without pre-built maps. Considering that IMUs can capture human poses but always drift for long-period use, while LiDAR is stable for global localization but rough for local positions and orientations, HSC4D makes both sensors complement each other by a joint optimization and achieves promising results for long-term capture. Relationships between humans and environments are also explored to make their interaction more realistic. To facilitate many down-stream tasks, like AR, VR, robots, autonomous driving, etc., we propose a dataset containing three large scenes (1k-5k $m^2$) with accurate dynamic human motions and locations. Diverse scenarios (climbing gym, multi-story building, slope, etc.) and challenging human activities (exercising, walking up/down stairs, climbing, etc.) demonstrate the effectiveness and the generalization ability of HSC4D. The dataset and code are available at http://www.lidarhumanmotion.net/hsc4d/.Comment: 10 pages, 8 figures, CVPR202

RF-Net: An End-to-End Image Matching Network based on Receptive Field

This paper proposes a new end-to-end trainable matching network based on receptive field, RF-Net, to compute sparse correspondence between images. Building end-to-end trainable matching framework is desirable and challenging. The very recent approach, LF-Net, successfully embeds the entire feature extraction pipeline into a jointly trainable pipeline, and produces the state-of-the-art matching results. This paper introduces two modifications to the structure of LF-Net. First, we propose to construct receptive feature maps, which lead to more effective keypoint detection. Second, we introduce a general loss function term, neighbor mask, to facilitate training patch selection. This results in improved stability in descriptor training. We trained RF-Net on the open dataset HPatches, and compared it with other methods on multiple benchmark datasets. Experiments show that RF-Net outperforms existing state-of-the-art methods.Comment: 9 pages, 6 figure

Semantic Labeling of Mobile LiDAR Point Clouds via Active Learning and Higher Order MRF

【Abstract】Using mobile Light Detection and Ranging point clouds to accomplish road scene labeling tasks shows promise for a variety of applications. Most existing methods for semantic labeling of point clouds require a huge number of fully supervised point cloud scenes, where each point needs to be manually annotated with a specific category. Manually annotating each point in point cloud scenes is labor intensive and hinders practical usage of those methods. To alleviate such a huge burden of manual annotation, in this paper, we introduce an active learning method that avoids annotating the whole point cloud scenes by iteratively annotating a small portion of unlabeled supervoxels and creating a minimal manually annotated training set. In order to avoid the biased sampling existing in traditional active learning methods, a neighbor-consistency prior is exploited to select the potentially misclassified samples into the training set to improve the accuracy of the statistical model. Furthermore, lots of methods only consider short-range contextual information to conduct semantic labeling tasks, but ignore the long-range contexts among local variables. In this paper, we use a higher order Markov random field model to take into account more contexts for refining the labeling results, despite of lacking fully supervised scenes. Evaluations on three data sets show that our proposed framework achieves a high accuracy in labeling point clouds although only a small portion of labels is provided. Moreover, comparative experiments demonstrate that our proposed framework is superior to traditional sampling methods and exhibits comparable performance to those fully supervised models.10.13039/501100001809-National Natural Science Foundation of China; Collaborative Innovation Center of Haixi Government Affairs Big Data Sharin

Pairwise registration of TLS point clouds by deep multi-scale local features

Abstract(#br)Because of the mechanism of TLS system, noise, outliers, various occlusions, varying cloud densities, etc. inevitably exist in the collection of TLS point clouds. To achieve automatic TLS point cloud registration, many methods, based on the hand-crafted features of keypoints, have been proposed. Despite significant progress, the current methods still face great challenges in accomplishing TLS point cloud registration. In this paper, we propose a multi-scale neural network to learn local shape descriptors for establishing correspondences between pairwise TLS point clouds. To train our model, data augmentation, developed on pairwise semi-synthetic 3D local patches, is to extend our network to be robust to rotation transformation. Then, based on varying local neighborhoods, multi-scale subnetworks are constructed and fused to learn robust local features. Experimental results demonstrate that our proposed method successfully registers two TLS point clouds and outperforms state-of-the-art methods. Besides, our learned descriptors are invariant to translation and tolerant to changes in rotation

Image-based orchard insect automated identification and classification method

Department of Entomology, Michigan State University; USDA-ARS Post-harvest Lab, East Lansing, MIInsect identification and classification is time-consuming work requiring expert knowledge for integrated pest management in orchards. An image-based automated insect identification and classification method is described in the paper. The complete method includes three models. An invariant local feature model was built for insect identification and classification using affine invariant local features; a global feature model was built for insect identification and classification using 54 global features; and a hierarchical combination model was proposed based on local feature and global feature models to combine advantages of the two models and increase performance. The three models were applied and tested for insect classification on eight insect species from pest colonies and orchards. The hierarchical combination model yielded better performance over global and local models. Moreover, to study the pose change of insects on traps and the hypothesis that an optimal time to acquire and image after landing exists, advanced analysis on time-dependent pose change of insects on traps is included in this study. The experimental results on field insect image classification with field-based images for training achieved the classification rate of 86.6% when testing with the combination model. This demonstrates the image-based insect identification and classification method could be a potential way for automated insect classification in integrated pest management. (C) 2012 Elsevier B.V. All rights reserved

Pose estimation-dependent identification method for field moth images using deep learning architecture

Due to the varieties of moth poses and cluttered background, traditional methods for automated identification of on-trap moths suffer problems of incomplete feature extraction and misidentification. A novel pose estimation-dependent automated identification method using deep learning architecture is proposed in this paper for on-trap field moth sample images. To deal with cluttered background and uneven illumination, two-level automated moth segmentation was created for separating moth sample images from each trap image. Moth pose was then estimated in terms of either top view or side view. Suitable combinations of texture, colour, shape and local features were extracted for further moth description. Finally, the improved pyramidal stacked de-noising auto-encoder (IpSDAE) architecture was proposed to build a deep neural network for moth identification. The experimental results on 762 field moth samples by 10-fold cross-validation achieved a good identification accuracy of 96.9%, and indicated that the deployment of the proposed pose estimation process is effective for automated moth identification