ICRA2018 SLAM论文

本文首发于泡泡机器人SLAM微信公众号。ICRA ,全名是IEEE International Conference on Robotics and Automation,相信大家都不陌生。今年ICRA2018（5.21-5.25）在美丽的澳大利亚布里斯班举行，作为机器人领域的顶级会议，虽然未能亲临现场，但是论文还是要读的，因此整理了一下与SLAM相关的一些论文，发现SLAM相关的论文确实不少，有70来篇，初略的看了一下，发现今年VIO的文章相对多一些，可能比较关注的是基于DSO的一篇文章，估计有不少同学也在做吧。还有就是与深度学习结合的一些文章，应该也有不少同学关注，大概看了一些深度学习结合，这里有用来估计位姿，用来做闭环检测（场景的再识别）等一些思路。还有就是SLAM的一些共性技术，比如优化、数据关联等，也有不少精彩的论文。这里按照文章的侧重点，对论文进行了一下分类，主要分为SLAM系统、VO/VIO、语义SLAM/语义地图、激光SLAM、水下SLAM、拓扑地图、结合深度学习、场景识别、数据关联、光度校正、事件相机、场景识别和其他的一些SLAM相关方向。

SLAM系统

今年SLAM系统一共有7篇论文，里面有的论文是基于地磁做了一个SLAM系统，有的提出了一个轻量级的SLAM系统,，也有针对手持设备提出的SLAM系统，还有一篇论文提出了解决运动速度过快，图像模糊而跟踪失败的问题方案，还有orbslam2的一篇论文，最开始还以为自己看错了，然后看了下，还真是orbslam2，具体有哪些改进，还没来得及看。具体的论文如下：

[1] iMag: Accurate and Rapidly Deployable Inertial Magneto-Inductive Localisation

[2] ProSLAM: Graph SLAM from a Programmer’s Perspective

[3] Constructing Category-Specific Models for Monocular Object-SLAM

[4] A Monocular SLAM System Leveraging Structural Regularity in Manhattan World

[5] Complementary Perception for Handheld SLAM

[6] MIS-SLAM: Real-time Large Scale Dense Deformable SLAM System in Minimal Invasive Surgery Based on Heterogeneous Computing

[7] ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo,and RGB-D Cameras

VO/VIO

这估计是今年比较火热的一个方向，感觉论文不少，比较关注的可能是基于DSO的imu融合SLAM系统吧！还有一篇以线条来指导关键点的选择，而不作为特征的论文，也比较有意思。还有一篇对VIO性能测评的文章，看上去好像也不错。

[1] Detection and Resolution of Motion Conflict in Visual Inertial Odometry

[2] Direct Sparse Visual-Inertial Odometry using Dynamic Marginalization

[3] Direct Line Guidance Odometry

[4] PIRVS: An Advanced Visual-Inertial SLAM System with Flexible Sensor Fusion and Hardware Co-Design

[5] Dense Planar-Inertial SLAM with Structural Constraints

[6] Low-Drift Visual Odometry in Structured Environments by Decoupling Rotational and Translational Motion

[7] Online Initialization and Automatic Camera-IMU Extrinsic Calibration for Monocular Visual-Inertial SLAM

[8] Relocalization, Global Optimization and Map Merging for Monocular Visual-Inertial SLAM

[9] Towards Globally Consistent Visual-Inertial Collaborative SLAM

[10] Visual odometry using a homography formulation with decoupled rotation and translation estimation using minimal solutions

[11] Encoderless Gimbal Calibration of Dynamic Multi-Camera Clusters

[12] A Benchmark Comparison of Monocular Visual-Inertial Odometry Algorithms for Flying Robots

[13] Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight

语义SLAM/语义地图

整体来看，今年语义SLAM的论文好像没有去年力度大，但是也有一些工作，关注于多帧之间的语义数据融合，还有用语义数据来校正回环检测的，我觉得这些工作才是真正的把语义数据逐渐的利用了起来，而不是像之前的一些论文，直接递归贝叶斯，然后加一个CRF搞一下，就说是语义SLAM。具体论文如下：

[1] Multi-view 3D Entangled Forest For Semantic Segmentation and Mapping

[2] A method to segment maps from different modalities using free space layout MAORIS: map of ripples segmentation

[3] Bayesian Scale Estimation for Monocular SLAM Based on Generic Object Detection for Correcting Scale Drift

[4] Semantic Mapping with Omnidirectional Vision

[5] Localization of Classified Objects in SLAM using Nonparametric Statistics and Clustering

激光SLAM

激光SLAM，今年论文好像不是很多，但是关于点云处理的有一些，3D和2D数据融合的也有一些。

[1] Efficient Continuous-time SLAM for 3D Lidar-based Online Mapping

[2] Elastic LiDAR Fusion: Dense Map-Centric Continuous-Time SLAM

[3] Direct Visual SLAM using Sparse Depth for Camera-LiDAR System

[4] IMLS-SLAM: scan-to-model matching based on 3D data

水下SLAM

之前经常看到有人问有没有关于水下SLAM的文章，今年ICRA就有，一共有三篇文章，利用水下的声学数据做SLAM，还有声呐成像等一些相关技术，具体可以去看论文。

[1] Sonar Visual Inertial SLAM of Underwater Structures

[2] Feature-based SLAM for Imaging Sonar with Under-constrained Landmarks

[3] Pose-graph SLAM using Forward-looking Sonar

拓扑地图

拓扑地图作为导航的一种重要地图类型，对于SLAM而言还是具有一定的参考价值，因为对这个也比较感兴趣，所以单独列出来了，大家可以看看。

[1] Topomap: Topological Mapping and Navigation Based on Visual SLAM Maps

[2] Topological Multi-Robot Belief Space Planning in Unknown Environments

[3] Feature-constrained Active Visual SLAM for Mobile Robot Navigation

结合深度学习

深度学习可谓是近几年来一个非常火热的领域了，深度学习在图像复原、图像分类、分割等领域都取得了突破性进展，但是在2D到3D的视觉研究上面一直也没有取得突破性的进展。今年ICRA也有很多深度学习相关的论文，这里主要关注与SLAM结合的领域。今年的深度学习与SLAM结合的领域有：估计位姿、场景的重识别（加语义信息的定位）、对高动态环境自适应等一些领域，具体论文如下：

[1] UnDeepVO: Monocular Visual Odometry through Unsupervised Deep

Learning

[2] Long-term Visual Localization using Semantically Segmented Images

[3] Learning-based Image Enhancement for Visual Odometry in Challenging

HDR Environments

[4] Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments

[6] Addressing Challenging Place Recognition Tasks using Generative Adversarial Networks

[7] Geometric Correspondence Network for Camera Motion Estimation

[8] LS-VO: Learning Dense Optical Subspace for Robust Visual Odometry

Estimation

场景识别

这里的场景识别，是有两篇不是基于深度学习的场景识别算法，也可以借鉴一下。

[1] Viewpoint-tolerant Place Recognition combining 2D and 3D information

for UAV navigation

[2] Omnidirectional Multisensory Perception Fusion for Long-Term Place Recognition

数据关联

数据关联其实是SLAM系统的一个致命弱点，宁愿不关联，也不能错误关联。我个人觉得，如果我们能够将前端数据关联部分和位姿计算部分统一起来，其实是能够避免这个问题的，因为误关联造成系统崩掉的原因在于，数据关联和位姿估计是两个独立的部分，位姿估计的时候，假设关联是正确的，如果能够解决这个问题，我觉得应该可以对这块有所改善。所以数据关联其实是一个非常重要的模块。今年ICRA上也有一些讨论的文章，大家可以看一下：

[1] A General Framework for Flexible Multi-Cue Photometric Point Cloud Registration

[2] Pairwise Consistent Measurement Set Maximization for Robust Multi-robot Map Merging

[3] Data-Efficient Decentralized Visual SLAM

[4] Conditional Compatibility Branch and Bound for Feature Cloud Matching

[5] Graph Correspondence-based Point Set Registration

光度校正

光度校正是视觉里面的一个非常重要的话题，这算是对数据的一个预处理吧，今年视觉领域的best paper提名，其中就有一篇光度校正的文章，大家可以看看：

[1] Online Photometric Calibration of Auto Exposure Video for Realtime Visual Odometry and SLAM

[2] Exposure Control using Bayesian Optimization based on Entropy Weighted Image Gradient

事件相机

新的传感器的出现，肯定能够带动一些SLAM方法的发展，事件相机以及优越的性能受到了研究者的关注，今年ICRA有两篇论文与此有关：

[1] Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios

[2] The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception

优化方法

SLAM的优化是SLAM的基础理论，主要还是关注稀疏性、增量更新、如何消除计算的复杂度、位姿图等方面。

[1] Complexity Analysis and Efficient Measurement Selection Primitives for High-Rate Graph SLAM

[2] AprilSAM: Real-time Smoothing and Mapping

[3] Fast Nonlinear Approximation of Pose Graph Node Marginalization

[4] A Linear Least Square Initialization Method for 3D Pose Graph Optimization Problem

[5] Convex Relaxations for Pose Graph Optimization with Outliers

[6] BAFS: Bundle Adjustment with Feature Scale Constraints for Enhanced Estimation Accuracy

[7] Robust Incremental SLAM under Constrained Optimization Formulation

[8] Graph SLAM sparsification with populated topologies using factor descent

Optimization

其他

这里包括一些三角化、定位、特征编码、动态场景、回环检测等与SLAM相关的技术，具体论文如下：

[1] Robust Visual Localization for Hopping Rovers on Small Bodies

[2] Sparse Gaussian Processes on Matrix Lie Groups: A Unified Framework for Optimizing Continuous-Time Trajectories

[3] Selection and Compression of Local Binary Features for Remote Visual SLAM

[4] Live Structural Modeling using RGB-D SLAM

[5] SLAMBench2: Multi-Objective Head-to-Head Benchmarking for Visual SLAM

[6] Feature-constrained Active Visual SLAM for Mobile Robot Navigation

[7] StaticFusion: Background Reconstruction for Dense RGB-D SLAM in Dynamic Environments

[8] Assigning Visual Words to Places for Loop Closure Detection

[9] Aided Inertial Navigation with Geometric Features: Observability Analysis

[10] Efficient Active SLAM based on Submap Joining, Graph Topology and Convex Optimization

[11] 2D SLAM Correction Prediction in Large Scale Urban Environments

[12] AA-ICP: Iterative Closest Point with Anderson Acceleration

[13] Talk Resource-Efficiently to Me: Optimal Communication Planning for Distributed Loop Closure Detection

[14] Eliminating Scale Drift in Monocular SLAM using Depth from Defocus

[15] Stability-Based Scale Estimation for Monocular SLAM

[16] Ef cient Octree-Based Volumetric SLAM Supporting Signed-Distance and Occupancy Mapping

[17] Incremental Segment-Based Localization in 3D Point Clouds

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