804 research outputs found
Using Unsupervised Deep Learning Technique for Monocular Visual Odometry
Deep learning technique-based visual odometry systems have recently shown promising results compared to feature matching-based methods. However, deep learning-based systems still require the ground truth poses for training and the additional knowledge to obtain absolute scale from monocular images for reconstruction. To address these issues, this paper presents a novel visual odometry system based on a recurrent convolutional neural network. The system employs an unsupervised end-to-end training approach. The depth information of scenes is used alongside monocular images to train the network in order to inject scale. Poses are inferred only from monocular images, thus making the proposed visual odometry system a monocular one. The experiments are conducted and the results show that the proposed method performs better than other monocular visual odometry systems. This paper has made two main contributions: 1) the creation of the unsupervised training framework in which the camera ground truth poses are only deployed for system performance evaluation rather than for training and 2) the absolute scale could be recovered without the post-processing of poses
GANVO: Unsupervised Deep Monocular Visual Odometry and Depth Estimation with Generative Adversarial Networks
In the last decade, supervised deep learning approaches have been extensively
employed in visual odometry (VO) applications, which is not feasible in
environments where labelled data is not abundant. On the other hand,
unsupervised deep learning approaches for localization and mapping in unknown
environments from unlabelled data have received comparatively less attention in
VO research. In this study, we propose a generative unsupervised learning
framework that predicts 6-DoF pose camera motion and monocular depth map of the
scene from unlabelled RGB image sequences, using deep convolutional Generative
Adversarial Networks (GANs). We create a supervisory signal by warping view
sequences and assigning the re-projection minimization to the objective loss
function that is adopted in multi-view pose estimation and single-view depth
generation network. Detailed quantitative and qualitative evaluations of the
proposed framework on the KITTI and Cityscapes datasets show that the proposed
method outperforms both existing traditional and unsupervised deep VO methods
providing better results for both pose estimation and depth recovery.Comment: ICRA 2019 - accepte
Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos
Learning to predict scene depth from RGB inputs is a challenging task both
for indoor and outdoor robot navigation. In this work we address unsupervised
learning of scene depth and robot ego-motion where supervision is provided by
monocular videos, as cameras are the cheapest, least restrictive and most
ubiquitous sensor for robotics.
Previous work in unsupervised image-to-depth learning has established strong
baselines in the domain. We propose a novel approach which produces higher
quality results, is able to model moving objects and is shown to transfer
across data domains, e.g. from outdoors to indoor scenes. The main idea is to
introduce geometric structure in the learning process, by modeling the scene
and the individual objects; camera ego-motion and object motions are learned
from monocular videos as input. Furthermore an online refinement method is
introduced to adapt learning on the fly to unknown domains.
The proposed approach outperforms all state-of-the-art approaches, including
those that handle motion e.g. through learned flow. Our results are comparable
in quality to the ones which used stereo as supervision and significantly
improve depth prediction on scenes and datasets which contain a lot of object
motion. The approach is of practical relevance, as it allows transfer across
environments, by transferring models trained on data collected for robot
navigation in urban scenes to indoor navigation settings. The code associated
with this paper can be found at https://sites.google.com/view/struct2depth.Comment: Thirty-Third AAAI Conference on Artificial Intelligence (AAAI'19
๋ฅ๋ฌ๋์ ๊ธฐ์ดํ ํจ๊ณผ์ ์ธ Visual Odometry ๊ฐ์ ๋ฐฉ๋ฒ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ์ ๊ธฐยท์ ๋ณด๊ณตํ๋ถ, 2020. 8. ์ด๋ฒํฌ.Understanding the three-dimensional environment is one of the most important issues in robotics and computer vision. For this purpose, sensors such as a lidar, a ultrasound, infrared devices, an inertial measurement unit (IMU) and cameras are used, individually or simultaneously, through sensor fusion. Among these sensors, in recent years, researches for use of visual sensors, which can obtain a lot of information at a low price, have been actively underway.
Understanding of the 3D environment using cameras includes depth restoration, optical/scene flow estimation, and visual odometry (VO). Among them, VO estimates location of a camera and maps the surrounding environment, while a camera-equipped robot or person travels. This technology must be preceded by other tasks such as path planning and collision avoidance. Also, it can be applied to practical applications such as autonomous driving, augmented reality (AR), unmanned aerial vehicle (UAV) control, and 3D modeling.
So far, researches on various VO algorithms have been proposed. Initial VO researches were conducted by filtering poses of robot and map features. Because of the disadvantage of the amount of computation being too large and errors are accumulated, a method using a keyframe was studied. Traditional VO can be divided into a feature-based method and a direct method. Methods using features obtain pose transformation between two images through feature extraction and matching. Direct methods directly compare the intensity of image pixels to obtain poses that minimize the sum of photometric errors.
Recently, due to the development of deep learning skills, many studies have been conducted to apply deep learning to VO. Deep learning-based VO, like other fields using deep learning with images, first extracts convolutional neural network (CNN) features and calculates pose transformation between images. Deep learning-based VO can be divided into supervised learning-based and unsupervised learning-based. For VO, using supervised learning, a neural network is trained using ground truth poses, and the unsupervised learning-based method learns poses using only image sequences without given ground truth values.
While existing research papers show decent performance, the image datasets used in these studies are all composed of high quality and clear images obtained using expensive cameras. There are also algorithms that can be operated only if non-image information such as exposure time, nonlinear response functions, and camera parameters is provided. In order for VO to be more widely applied to real-world application problems, odometry estimation should be performed even if the datasets are incomplete. Therefore, in this dissertation, two methods are proposed to improve VO performance using deep learning.
First, I adopt a super-resolution (SR) technique to improve the performance of VO using images with low-resolution and noises. The existing SR techniques have mainly focused on increasing image resolution rather than execution time. However, a real-time property is very important for VO. Therefore, the SR network should be designed considering the execution time, resolution increment, and noise reduction in this case. Conducting a VO after passing through this SR network, a higher performance VO can be carried out, than using original images. Experimental results using the TUM dataset show that the proposed method outperforms the conventional VO and other SR methods.
Second, I propose a fully unsupervised learning-based VO that performs odometry estimation, single-view depth estimation, and camera intrinsic parameter estimation simultaneously using a dataset consisting only of image sequences. In the existing unsupervised learning-based VO, algorithms were performed using the images and intrinsic parameters of the camera. Based on existing the technique, I propose a method for additionally estimating camera parameters from the deep intrinsic network. Intrinsic parameters are estimated by two assumptions using the properties of camera parameters in an intrinsic network. Experiments using the KITTI dataset show that the results are comparable to those of the conventional method.3์ฐจ์ ํ๊ฒฝ์ ๋ํ ์ดํด๋ ๋ก๋ณดํฑ์ค์ ์ปดํจํฐ ๋น์ ๋ถ์ผ์์ ๊ต์ฅํ ์ค์ํ ๋ฌธ์ ์ค ํ๋์ด๋ค. ์ด๋ฅผ ์ํด ๋ผ์ด๋ค, ์ด์ํ, ์ ์ธ์ , inertial measurement unit (IMU), ์นด๋ฉ๋ผ ๋ฑ์ ์ผ์๊ฐ ๊ฐ๋ณ์ ์ผ๋ก ๋๋ ์ผ์ ์ตํฉ์ ํตํด ์ฌ๋ฌ ์ผ์๊ฐ ๋์์ ์ฌ์ฉ๋๊ธฐ๋ ํ๋ค. ์ด ์ค์์๋ ์ต๊ทผ์๋ ์๋์ ์ผ๋ก ์ ๋ ดํ ๊ฐ๊ฒฉ์ ๋ง์ ์ ๋ณด๋ฅผ ์ป์ ์ ์๋ ์นด๋ฉ๋ผ๋ฅผ ์ด์ฉํ ์ฐ๊ตฌ๊ฐ ํ๋ฐํ ์งํ๋๊ณ ์๋ค.
์นด๋ฉ๋ผ๋ฅผ ์ด์ฉํ 3์ฐจ์ ํ๊ฒฝ ์ธ์ง๋ ๊น์ด ๋ณต์, optical/scene flow ์ถ์ , visual odometry (VO) ๋ฑ์ด ์๋ค. ์ด ์ค VO๋ ์นด๋ฉ๋ผ๋ฅผ ์ฅ์ฐฉํ ๋ก๋ด ํน์ ์ฌ๋์ด ์ด๋ํ๋ฉฐ ์์ ์ ์์น๋ฅผ ํ์
ํ๊ณ ์ฃผ๋ณ ํ๊ฒฝ์ ์ง๋๋ฅผ ์์ฑํ๋ ๊ธฐ์ ์ด๋ค. ์ด ๊ธฐ์ ์ ๊ฒฝ๋ก ์ค์ , ์ถฉ๋ ํํผ ๋ฑ ๋ค๋ฅธ ์๋ฌด๋ฅผ ์ํํ๊ธฐ ์ ์ ํ์์ ์ผ๋ก ์ ํ๋์ด์ผ ํ๋ฉฐ ์์จ ์ฃผํ, AR, UAV contron, 3D modelling ๋ฑ ์ค์ ์์ฉ ๋ฌธ์ ์ ์ ์ฉ๋ ์ ์๋ค.
ํ์ฌ ๋ค์ํ VO ์๊ณ ๋ฆฌ์ฆ์ ๋ํ ๋
ผ๋ฌธ์ด ์ ์๋์๋ค. ์ด๊ธฐ VO ์ฐ๊ตฌ๋ feature๋ฅผ ์ด์ฉํ์ฌ feature์ ๋ก๋ด์ pose๋ฅผ ํํฐ๋ง ํ๋ ๋ฐฉ์์ผ๋ก ์งํ๋์๋ค. ํํฐ๋ฅผ ์ด์ฉํ ๋ฐฉ๋ฒ์ ๊ณ์ฐ๋์ด ๋๋ฌด ๋ง๊ณ ์ค์ฐจ๊ฐ ๋์ ๋๋ค๋ ๋จ์ ๋๋ฌธ์ keyframe์ ์ด์ฉํ๋ ๋ฐฉ๋ฒ์ด ์ฐ๊ตฌ๋์๋ค. ์ด ๋ฐฉ์์ผ๋ก feature๋ฅผ ์ด์ฉํ๋ ๋ฐฉ์๊ณผ ํฝ์
์ intensity๋ฅผ ์ง์ ์ฌ์ฉํ๋ direct ๋ฐฉ์์ด ์ฐ๊ตฌ๋์๋ค. feature๋ฅผ ์ด์ฉํ๋ ๋ฐฉ๋ฒ๋ค์ feature์ ์ถ์ถ๊ณผ ๋งค์นญ์ ์ด์ฉํ์ฌ ๋ ์ด๋ฏธ์ง ์ฌ์ด์ pose ๋ณํ๋ฅผ ๊ตฌํ๋ฉฐ direct ๋ฐฉ๋ฒ๋ค์ ์ด๋ฏธ์ง ํฝ์
์ intensity๋ฅผ ์ง์ ๋น๊ตํ์ฌ photometric error๋ฅผ ์ต์ํ ์ํค๋ pose๋ฅผ ๊ตฌํ๋ ๋ฐฉ์์ด๋ค.
์ต๊ทผ์๋ deep learning ์๊ณ ๋ฆฌ์ฆ์ ๋ฐ๋ฌ๋ก ์ธํด VO์๋ deep learning์ ์ ์ฉ์ํค๋ ์ฐ๊ตฌ๊ฐ ๋ง์ด ์งํ๋๊ณ ์๋ค. Deep learning-based VO๋ ์ด๋ฏธ์ง๋ฅผ ์ด์ฉํ ๋ค๋ฅธ ๋ถ์ผ์ ๊ฐ์ด ๊ธฐ๋ณธ์ ์ผ๋ก CNN์ ์ด์ฉํ์ฌ feature๋ฅผ ์ถ์ถํ ๋ค ์ด๋ฏธ์ง ์ฌ์ด์ pose ๋ณํ๋ฅผ ๊ณ์ฐํ๋ค. ์ด๋ ๋ค์ supervised learning์ ์ด์ฉํ ๋ฐฉ์๊ณผ unsupervised learning์ ์ด์ฉํ ๋ฐฉ๋ฒ์ผ๋ก ๋๋ ์ ์๋ค. supervised learning์ ์ด์ฉํ VO๋ pose์ ์ฐธ๊ฐ์ ์ฌ์ฉํ์ฌ ํ์ต์ ์ํค๋ฉฐ, unsupervised learning์ ์ด์ฉํ๋ ๋ฐฉ๋ฒ์ ์ฃผ์ด์ง๋ ์ฐธ๊ฐ ์์ด ์ด๋ฏธ์ง์ ์ ๋ณด๋ง์ ์ด์ฉํ์ฌ pose๋ฅผ ํ์ต์ํค๋ ๋ฐฉ์์ด๋ค.
๊ธฐ์กด VO ๋
ผ๋ฌธ๋ค์ ์ข์ ์ฑ๋ฅ์ ๋ณด์์ง๋ง ์ฐ๊ตฌ์ ์ฌ์ฉ๋ ์ด๋ฏธ์ง dataset๋ค์ ๋ชจ๋ ๊ณ ๊ฐ์ ์นด๋ฉ๋ผ๋ฅผ ์ด์ฉํ์ฌ ์ป์ด์ง ๊ณ ํ์ง์ ์ ๋ช
ํ ์ด๋ฏธ์ง๋ค๋ก ๊ตฌ์ฑ๋์ด ์๋ค. ๋ํ ๋
ธ์ถ ์๊ฐ, ๋น์ ํ ๋ฐ์ ํจ์, ์นด๋ฉ๋ผ ํ๋ผ๋ฏธํฐ ๋ฑ์ ์ด๋ฏธ์ง ์ธ์ ์ธ ์ ๋ณด๋ฅผ ์ด์ฉํด์ผ๋ง ์๊ณ ๋ฆฌ์ฆ์ ๋์์ด ๊ฐ๋ฅํ๋ค. VO๊ฐ ์ค์ ์์ฉ ๋ฌธ์ ์ ๋ ๋๋ฆฌ ์ ์ฉ๋๊ธฐ ์ํด์๋ dataset์ด ๋ถ์์ ํ ๊ฒฝ์ฐ์๋ odometry ์ถ์ ์ด ์ ์ด๋ฃจ์ด์ ธ์ผ ํ๋ค. ์ด์ ๋ณธ ๋
ผ๋ฌธ์์๋ deep learning์ ์ด์ฉํ์ฌ VO์ ์ฑ๋ฅ์ ๋์ด๋ ๋ ๊ฐ์ง ๋ฐฉ๋ฒ์ ์ ์ํ์๋ค.
์ฒซ ๋ฒ์งธ๋ก๋ super-resolution (SR) ๊ธฐ๋ฒ์ผ๋ก ์ ํด์๋, ๋
ธ์ด์ฆ๊ฐ ํฌํจ๋ ์ด๋ฏธ์ง๋ฅผ ์ด์ฉํ VO์ ์ฑ๋ฅ์ ๋์ด๋ ๋ฐฉ๋ฒ์ ์ ์ํ๋ค. ๊ธฐ์กด์ SR ๊ธฐ๋ฒ์ ์ํ ์๊ฐ๋ณด๋ค๋ ์ด๋ฏธ์ง์ ํด์๋๋ฅผ ํฅ์์ํค๋ ๋ฐฉ๋ฒ์ ์ฃผ๋ก ์ง์คํ์๋ค. ํ์ง๋ง VO ์ํ์ ์์ด์๋ ์ค์๊ฐ์ฑ์ด ๊ต์ฅํ ์ค์ํ๋ค. ๋ฐ๋ผ์ ์ํ ์๊ฐ์ ๊ณ ๋ คํ SR ๋คํธ์ํฌ์ ์ค๊ณํ์ฌ ์ด๋ฏธ์ง์ ํด์๋๋ฅผ ๋์ด๊ณ ๋
ธ์ด์ฆ๋ฅผ ์ค์๋ค. ์ด SR ๋คํธ์ํฌ๋ฅผ ํต๊ณผ์ํจ ๋ค VO๋ฅผ ์ํํ๋ฉด ๊ธฐ์กด์ ์ด๋ฏธ์ง๋ฅผ ์ฌ์ฉํ ๋๋ณด๋ค ๋์ ์ฑ๋ฅ์ VO๋ฅผ ์ค์๊ฐ์ผ๋ก ์ํํ ์ ์๋ค. TUM dataset์ ์ด์ฉํ ์คํ ๊ฒฐ๊ณผ ๊ธฐ์กด์ VO ๊ธฐ๋ฒ๊ณผ ๋ค๋ฅธ SR ๊ธฐ๋ฒ์ ์ ์ฉํ์์ ๋ ๋ณด๋ค ์ ์ํ๋ ๋ฐฉ๋ฒ์ ์ฑ๋ฅ์ด ๋ ๋์ ๊ฒ์ ํ์ธํ ์ ์์๋ค.
๋ ๋ฒ์งธ๋ก๋ ์ฐ์๋ ์ด๋ฏธ์ง๋ง์ผ๋ก ๊ตฌ์ฑ๋ dataset์ ์ด์ฉํ์ฌ VO, ๋จ์ผ ์ด๋ฏธ์ง ๊น์ด ์ถ์ , ์นด๋ฉ๋ผ ๋ด๋ถ ํ๋ผ๋ฏธํฐ ์ถ์ ์ ์ํํ๋ fully unsupervised learning-based VO๋ฅผ ์ ์ํ๋ค. ๊ธฐ์กด unsupervised learning์ ์ด์ฉํ VO์์๋ ์ด๋ฏธ์ง๋ค๊ณผ ์ด๋ฏธ์ง๋ฅผ ์ดฌ์ํ ์นด๋ฉ๋ผ์ ๋ด๋ถ ํ๋ผ๋ฏธํฐ๋ฅผ ์ด์ฉํ์ฌ VO๋ฅผ ์ํํ์๋ค. ์ด ๊ธฐ์ ์ ๊ธฐ๋ฐ์ผ๋ก ๋ณธ ๋
ผ๋ฌธ์์๋ deep intrinsic ๋คํธ์ํฌ๋ฅผ ์ถ๊ฐํ์ฌ ์นด๋ฉ๋ผ ํ๋ผ๋ฏธํฐ๊น์ง ๋คํธ์ํฌ์์ ์ถ์ ํ๋ ๋ฐฉ๋ฒ์ ์ ์ํ๋ค. 0์ผ๋ก ์๋ ดํ๊ฑฐ๋ ์ฝ๊ฒ ๋ฐ์ฐํ๋ intrinsic ๋คํธ์ํฌ์ ์นด๋ฉ๋ผ ํ๋ผ๋ฏธํฐ์ ์ฑ์ง์ ์ด์ฉํ ๋ ๊ฐ์ง ๊ฐ์ ์ ํตํด ๋ด๋ถ ํ๋ผ๋ฏธํฐ๋ฅผ ์ถ์ ํ ์ ์์๋ค. KITTI dataset์ ์ด์ฉํ ์คํ์ ํตํด intrinsic parameter ์ ๋ณด๋ฅผ ์ ๊ณต๋ฐ์ ์งํ๋ ๊ธฐ์กด์ ๋ฐฉ๋ฒ๊ณผ ์ ์ฌํ ์ฑ๋ฅ์ ํ์ธํ ์ ์์๋ค.1 INTRODUCTION 1
1.1 Background and Motivation 1
1.2 Literature Review 3
1.3 Contributions 10
1.4 Thesis Structure 11
2 Mathematical Preliminaries of Visual Odometry 13
2.1 Feature-based VO 13
2.2 Direct VO 17
2.3 Learning-based VO 21
2.3.1 Supervised learning-based VO 22
2.3.2 Unsupervised learning-based VO 25
3 Error Improvement in Visual Odometry Using Super-resolution 29
3.1 Introduction 29
3.2 Related Work 31
3.2.1 Visual Odometry 31
3.2.2 Super-resolution 33
3.3 SR-VO 34
3.3.1 VO performance analysis according to changing resolution 34
3.3.2 Super-Resolution Network 37
3.4 Experiments 40
3.4.1 Super-Resolution Procedure 40
3.4.2 VO with SR images 42
3.5 Summary 54
4 Visual Odometry Enhancement Method Using Fully Unsupervised Learning 55
4.1 Introduction 55
4.2 Related Work 57
4.2.1 Traditional Visual Odometry 57
4.2.2 Single-view Depth Recovery 58
4.2.3 Supervised Learning-based Visual Odometry 59
4.2.4 Unsupervised Learning-based Visual Odometry 60
4.2.5 Architecture Overview 62
4.3 Methods 62
4.3.1 Predicting the Target Image using Source Images 62
4.3.2 Intrinsic Parameters Regressor 63
4.4 Experiments 66
4.4.1 Monocular Depth Estimation 66
4.4.2 Visual Odometry 67
4.4.3 Intrinsic Parameters Estimation 77
5 Conclusion and Future Work 82
5.1 Conclusion 82
5.2 Future Work 85
Bibliography 86
Abstract (In Korean) 101Docto
Monocular Visual Inertial Odometry using Learning-based Methods
Precise pose information is a fundamental prerequisite for numerous applications in robotics, Artificial Intelligent and mobile computing. Many well-developed algorithms have been established using a single sensor or multiple sensors. Visual Inertial Odometry (VIO) uses images and inertial measurements to estimate the motion and is considered a key technology for GPS-denied localization in the real world and also virtual reality and augmented reality.
This study develops three novel learning-based approaches to Odometry estimation using a monocular camera and inertial measurement unit. The networks are well-trained on standard datasets, KITTI and EuROC, and a custom dataset using supervised, unsupervised and semi-supervised training methods. Compared to traditional methods, the deep-learning methods presented here do not require precise manual synchronization of the camera and IMU or explicit camera calibration.
To the best of our knowledge, the proposed supervised method is a novel end-to-end trainable Visual-Inertial Odometry method with an IMU pre-integration module,that simplifies the network architecture and reduces the computation cost. Meanwhile, the unsupervised Visual-Inertial Odometry method shows its novelty in achieving outstanding accuracy in Odometry estimation while training with monocular images and inertial measurements only. Last but not least, the semi-supervised method is the first VisualInertial Odometry approach that uses a semi-supervised training technique in the literature, allowing the network to learn from both labeled and unlabeled datasets.
Through our qualitative and quantitative experimentation on a wide range of datasets, we conclude that the proposed methods can be used to obtain accurate visual localization information to a wide variety of consumer devices and robotic platforms
Pose Graph Optimization for Unsupervised Monocular Visual Odometry
Unsupervised Learning based monocular visual odometry (VO) has lately drawn
significant attention for its potential in label-free leaning ability and
robustness to camera parameters and environmental variations. However,
partially due to the lack of drift correction technique, these methods are
still by far less accurate than geometric approaches for large-scale odometry
estimation. In this paper, we propose to leverage graph optimization and loop
closure detection to overcome limitations of unsupervised learning based
monocular visual odometry. To this end, we propose a hybrid VO system which
combines an unsupervised monocular VO called NeuralBundler with a pose graph
optimization back-end. NeuralBundler is a neural network architecture that uses
temporal and spatial photometric loss as main supervision and generates a
windowed pose graph consists of multi-view 6DoF constraints. We propose a novel
pose cycle consistency loss to relieve the tensions in the windowed pose graph,
leading to improved performance and robustness. In the back-end, a global pose
graph is built from local and loop 6DoF constraints estimated by NeuralBundler
and is optimized over SE(3). Empirical evaluation on the KITTI odometry dataset
demonstrates that 1) NeuralBundler achieves state-of-the-art performance on
unsupervised monocular VO estimation, and 2) our whole approach can achieve
efficient loop closing and show favorable overall translational accuracy
compared to established monocular SLAM systems.Comment: Accepted to ICRA'201
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
We consider the problem of dense depth prediction from a sparse set of depth
measurements and a single RGB image. Since depth estimation from monocular
images alone is inherently ambiguous and unreliable, to attain a higher level
of robustness and accuracy, we introduce additional sparse depth samples, which
are either acquired with a low-resolution depth sensor or computed via visual
Simultaneous Localization and Mapping (SLAM) algorithms. We propose the use of
a single deep regression network to learn directly from the RGB-D raw data, and
explore the impact of number of depth samples on prediction accuracy. Our
experiments show that, compared to using only RGB images, the addition of 100
spatially random depth samples reduces the prediction root-mean-square error by
50% on the NYU-Depth-v2 indoor dataset. It also boosts the percentage of
reliable prediction from 59% to 92% on the KITTI dataset. We demonstrate two
applications of the proposed algorithm: a plug-in module in SLAM to convert
sparse maps to dense maps, and super-resolution for LiDARs. Software and video
demonstration are publicly available.Comment: accepted to ICRA 2018. 8 pages, 8 figures, 3 tables. Video at
https://www.youtube.com/watch?v=vNIIT_M7x7Y. Code at
https://github.com/fangchangma/sparse-to-dens
- โฆ