ICRA,全名是IEEE International Conference on Robotics and Automation,相信泡芙们都不陌生。今年ICRA2017(5.29-6.3)在美丽的新加坡举行,主题是“Innovation, Entrepreneurship, and Real-world Solutions”,应该有不少泡芙亲临现场,欢迎大家在下方留言讲讲感受哈!



[1]Probabilistic Data Association for Semantic SLAM

[2]SemanticFusion: Dense 3D Semantic Mapping with Convolutional Neural Networks


[1]Visibility Enhancement for Underwater Visual SLAM based on Underwater Light Scattering Model

[2]Multi-UAV Collaborative Monocular SLAM

[3]Keyframe-based Dense Planar SLAM

[4]RGB-T SLAM: A Flexible SLAM Framework By Combining Appearance and Thermal Information

[5]Real-time Monocular Dense Mapping on Aerial Robots Using Visual-Inertial Fusion

[6]MonoRGBD-SLAM: Simultaneous Localization and Mapping Using Both Monocular and RGBD Cameras

[7]Real-time Local 3D Reconstruction for Aerial Inspection using Superpixel Expansion

[8]PL-SLAM: Real-Time Monocular Visual SLAM with Points and Lines

[9]RFM-SLAM: Exploiting Relative Feature

[10]Measurements to Separate Orientation and Position Estimation in SLAM

[11]Illumination Change Robustness in Direct Visual SLAM

[12]Monocular Visual Odometry: Sparse Joint Optimisation or Dense  Alternation

[13]RRD-SLAM: Radial-distorted Rolling-shutter Direct SLAM

[14]Application-oriented Design Space Exploration for SLAM Algorithms

[15]Convergence and Consistency Analysis for A 3D Invariant-EKF SLAM



[1]Robust Visual Localization in Changing Lighting Conditions

[2]Direct Monocular Odometry Using Points and Lines

[3]Accurate Stereo Visual Odometry with Gamma Distributions

[4]Semi-Dense Visual Odometry for RGB-D Cameras Using Approximate Nearest Neighbour Fields

[5]Direct Visual Odometry in Low Light using Binary Descriptors


[1]A Comparative Analysis of Tightly-coupled Monocular, Binocular, and Stereo VINS

[2]Attention and Anticipation in Fast Visual-Inertial Navigation

[3]High Altitude Monocular Visual-Inertial State Estimation:Initialization and Sensor Fusion

[4]Photometric Patch-based Visual-Inertial Odometry

[5]Fast Odometry and Scene Flow from RGB-D Cameras based on Geometric Clustering


[1]Overlap-based ICP Tuning for Robust Localization of a Humanoid Robot

[2]Point and line feature-based observer design on SL(3) for Homography

estimation and its application to image stabilization

[3]FLAG: Feature-based Localization between Air and Ground

[4]Gaussian Process Estimation of Odometry Errors for Localization and


[5]Random Forests versus Neural Networks −What’s Best for Camera Localization

[6]Fast Odometry and Scene Flow from RGB-D Cameras based on

Geometric Clustering

[7]Robust Localization and Localizability Estimation with a Rotating Laser Scanner


[1]Joint Pose and Principal Curvature Refinement Using Quadrics

[2]De-noising, Stabilizing and Completing 3D Reconstructions On-the-go using Plane Priors

[3]TSDF-based Change Detection for Consistent Long-Term Dense

[4]Reconstruction and Dynamic Object Discovery Map Quality Evaluation for Visual Localization


[1]Self-supervised Visual Descriptor Learning for Dense Correspondence

[2]Dense Monocular Reconstruction using Surface Normals

[3]Robust Stereo Matching with Surface Normal Prediction

[4]Reducing Drift in Visual Odometry by Inferring Sun Direction Using a Bayesian Convolutional Neural Network

[5]DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent

Convolutional Neural Networks

[6]Efficient Descriptor Learning for Large Scale Localization

[7]Semantics-aware Visual Localization under Challenging Perceptual Conditions

[8]Delving Deeper into Convolutional Neural Networks for Camera Relocalization


[1]Visual Place Recognition with Probabilistic Voting

[2]SegMatch: Segment Based Place Recognition in 3D Point Clouds

[3]Fast-SeqSLAM: A Fast Appearance Based Place Recognition Algorithm

[4]Deep Learning Features at Scale for Visual Place Recognition

[5]O-POCO: Online POint cloud COmpression mapping for visual odometry and SLAM

[6]3D Map Merging on Pose Graphs



[1]A Direct Formulation for Camera Calibration

[2]Visual-inertial Self-calibration on Informative Motion Segments

[3]A Branch-and-Bound Algorithm for Checkerboard Extraction in Camera-Laser Calibration

[4]Observability-Aware Trajectory Optimization for Self-Calibration with Application to UAVs

【Event Camera】

[1]EVO: A Geometric Approach to Event-based 6-DOF Parallel Tracking and Mapping in Real-time

[2]Accurate Angular Velocity Estimation with an Event Camera


【作者】 蔡育展【编辑】Reality

Apanchen mitglied registriert seit 27 juli 2014 Info zu den Jungs beiträge 22 zustimmungen 4 hi helena, erledigt viel erfolg

About the author: Bi-Ke Chen