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Visual Odometry

15 papers with code · Robots

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gvnn: Neural Network Library for Geometric Computer Vision

25 Jul 2016ankurhanda/gvnn

We introduce gvnn, a neural network library in Torch aimed towards bridging the gap between classic geometric computer vision and deep learning. Inspired by the recent success of Spatial Transformer Networks, we propose several new layers which are often used as parametric transformations on the data in geometric computer vision.

DEPTH ESTIMATION IMAGE RECONSTRUCTION VISUAL ODOMETRY

Learning Depth from Monocular Videos using Direct Methods

CVPR 2018 yzcjtr/GeoNet

The ability to predict depth from a single image - using recent advances in CNNs - is of increasing interest to the vision community. Unsupervised strategies to learning are particularly appealing as they can utilize much larger and varied monocular video datasets during learning without the need for ground truth depth or stereo.

VISUAL ODOMETRY

A General Optimization-based Framework for Local Odometry Estimation with Multiple Sensors

11 Jan 2019HKUST-Aerial-Robotics/VINS-Fusion

In this paper, we proposed a general optimization-based framework for odometry estimation, which supports multiple sensor sets. We validate the performance of our system on public datasets and through real-world experiments with multiple sensors.

VISUAL ODOMETRY

PL-SLAM: a Stereo SLAM System through the Combination of Points and Line Segments

26 May 2017rubengooj/pl-slam

In low-textured environments, though, it is often difficult to find a sufficient number of reliable point features and, as a consequence, the performance of such algorithms degrades. This paper proposes PL-SLAM, a stereo visual SLAM system that combines both points and line segments to work robustly in a wider variety of scenarios, particularly in those where point features are scarce or not well-distributed in the image.

VISUAL ODOMETRY

Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction

CVPR 2018 Huangying-Zhan/Depth-VO-Feat

Despite learning based methods showing promising results in single view depth estimation and visual odometry, most existing approaches treat the tasks in a supervised manner. In this paper, we explore the use of stereo sequences for learning depth and visual odometry.

DEPTH AND CAMERA MOTION MONOCULAR DEPTH ESTIMATION VISUAL ODOMETRY

Visual SLAM with Network Uncertainty Informed Feature Selection

29 Nov 2018navganti/SIVO

In order to facilitate long-term localization using a visual simultaneous localization and mapping (SLAM) algorithm, careful feature selection is required such that reference points persist over long durations and the runtime and storage complexity of the algorithm remain consistent. We present SIVO (Semantically Informed Visual Odometry and Mapping), a novel information-theoretic feature selection method for visual SLAM which incorporates machine learning and neural network uncertainty into the feature selection pipeline.

SIMULTANEOUS LOCALIZATION AND MAPPING VISUAL ODOMETRY

CNN-SVO: Improving the Mapping in Semi-Direct Visual Odometry Using Single-Image Depth Prediction

1 Oct 2018yan99033/CNN-SVO

Reliable feature correspondence between frames is a critical step in visual odometry (VO) and visual simultaneous localization and mapping (V-SLAM) algorithms. In comparison with existing VO and V-SLAM algorithms, semi-direct visual odometry (SVO) has two main advantages that lead to state-of-the-art frame rate camera motion estimation: direct pixel correspondence and efficient implementation of probabilistic mapping method.

DEPTH ESTIMATION MOTION ESTIMATION SIMULTANEOUS LOCALIZATION AND MAPPING VISUAL ODOMETRY

How to Train a CAT: Learning Canonical Appearance Transformations for Direct Visual Localization Under Illumination Change

9 Sep 2017utiasSTARS/cat-net

Direct visual localization has recently enjoyed a resurgence in popularity with the increasing availability of cheap mobile computing power. The competitive accuracy and robustness of these algorithms compared to state-of-the-art feature-based methods, as well as their natural ability to yield dense maps, makes them an appealing choice for a variety of mobile robotics applications.

TRANSFER LEARNING VISUAL LOCALIZATION VISUAL ODOMETRY

DPC-Net: Deep Pose Correction for Visual Localization

10 Sep 2017utiasSTARS/dpc-net

We present a novel method to fuse the power of deep networks with the computational efficiency of geometric and probabilistic localization algorithms. We use this loss to train a Deep Pose Correction network (DPC-Net) that predicts corrections for a particular estimator, sensor and environment.

VISUAL LOCALIZATION VISUAL ODOMETRY

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

20 Sep 2016utiasSTARS/sun-bcnn-vo

We present a method to incorporate global orientation information from the sun into a visual odometry pipeline using only the existing image stream, where the sun is typically not visible. We leverage recent advances in Bayesian Convolutional Neural Networks to train and implement a sun detection model that infers a three-dimensional sun direction vector from a single RGB image.

VISUAL ODOMETRY