5,542 research outputs found
Motion segmentation based robust RGB-D SLAM
© 2014 IEEE. A sparse feature-based motion segmentation algorithm for RGB-D data is proposed which offers us a unified way to handle outliers and dynamic scenarios. Together with the pose-graph SLAM framework, they constitute an effective and robust solution that enable us to do RGB-D SLAM in wide range of situations, although traditionally they have been divided into different categories and treated separately using different kinds of methods. Through comparisons with RANSAC using simulated data and testing with different benchmark RGB-D datasets against the state-of-the-art method in RGB-D SLAM, we show that our solution is efficient and effective in handling general static and dynamic scenarios, some of which have not be achieved before
Towards dense moving object segmentation based robust dense RGB-D SLAM in dynamic scenarios
© 2014 IEEE. Based on the latest achievements in computer vision and RGB-D SLAM, a practical way for dense moving object segmentation and thus a new framework for robust dense RGB-D SLAM in challenging dynamic scenarios is put forward. As the state-of-the-art method in RGB-D SLAM, dense SLAM is very robust when there are motion blur or featureless regions, while most of those sparse feature-based methods could not handle them. However, it is very susceptible to dynamic elements in the scenarios. To enhance its robustness in dynamic scenarios, we propose to combine dense moving object segmentation with dense SLAM. Since the object segmentation results from the latest available algorithm in computer vision are not satisfactory, we propose some effective measures to improve upon them so that better results can be achieved. After dense segmentation of dynamic objects, dense SLAM can be employed to estimate the camera poses. Quantitative results from the available challenging benchmark dataset have proved the effectiveness of our method
Visual Localization and Mapping in Dynamic and Changing Environments
The real-world deployment of fully autonomous mobile robots depends on a
robust SLAM (Simultaneous Localization and Mapping) system, capable of handling
dynamic environments, where objects are moving in front of the robot, and
changing environments, where objects are moved or replaced after the robot has
already mapped the scene. This paper presents Changing-SLAM, a method for
robust Visual SLAM in both dynamic and changing environments. This is achieved
by using a Bayesian filter combined with a long-term data association
algorithm. Also, it employs an efficient algorithm for dynamic keypoints
filtering based on object detection that correctly identify features inside the
bounding box that are not dynamic, preventing a depletion of features that
could cause lost tracks. Furthermore, a new dataset was developed with RGB-D
data especially designed for the evaluation of changing environments on an
object level, called PUC-USP dataset. Six sequences were created using a mobile
robot, an RGB-D camera and a motion capture system. The sequences were designed
to capture different scenarios that could lead to a tracking failure or a map
corruption. To the best of our knowledge, Changing-SLAM is the first Visual
SLAM system that is robust to both dynamic and changing environments, not
assuming a given camera pose or a known map, being also able to operate in real
time. The proposed method was evaluated using benchmark datasets and compared
with other state-of-the-art methods, proving to be highly accurate.Comment: 14 pages, 13 figure
Real-time Monocular Object SLAM
We present a real-time object-based SLAM system that leverages the largest
object database to date. Our approach comprises two main components: 1) a
monocular SLAM algorithm that exploits object rigidity constraints to improve
the map and find its real scale, and 2) a novel object recognition algorithm
based on bags of binary words, which provides live detections with a database
of 500 3D objects. The two components work together and benefit each other: the
SLAM algorithm accumulates information from the observations of the objects,
anchors object features to especial map landmarks and sets constrains on the
optimization. At the same time, objects partially or fully located within the
map are used as a prior to guide the recognition algorithm, achieving higher
recall. We evaluate our proposal on five real environments showing improvements
on the accuracy of the map and efficiency with respect to other
state-of-the-art techniques
Direct Monocular Odometry Using Points and Lines
Most visual odometry algorithm for a monocular camera focuses on points,
either by feature matching, or direct alignment of pixel intensity, while
ignoring a common but important geometry entity: edges. In this paper, we
propose an odometry algorithm that combines points and edges to benefit from
the advantages of both direct and feature based methods. It works better in
texture-less environments and is also more robust to lighting changes and fast
motion by increasing the convergence basin. We maintain a depth map for the
keyframe then in the tracking part, the camera pose is recovered by minimizing
both the photometric error and geometric error to the matched edge in a
probabilistic framework. In the mapping part, edge is used to speed up and
increase stereo matching accuracy. On various public datasets, our algorithm
achieves better or comparable performance than state-of-the-art monocular
odometry methods. In some challenging texture-less environments, our algorithm
reduces the state estimation error over 50%.Comment: ICRA 201
Fine-To-Coarse Global Registration of RGB-D Scans
RGB-D scanning of indoor environments is important for many applications,
including real estate, interior design, and virtual reality. However, it is
still challenging to register RGB-D images from a hand-held camera over a long
video sequence into a globally consistent 3D model. Current methods often can
lose tracking or drift and thus fail to reconstruct salient structures in large
environments (e.g., parallel walls in different rooms). To address this
problem, we propose a "fine-to-coarse" global registration algorithm that
leverages robust registrations at finer scales to seed detection and
enforcement of new correspondence and structural constraints at coarser scales.
To test global registration algorithms, we provide a benchmark with 10,401
manually-clicked point correspondences in 25 scenes from the SUN3D dataset.
During experiments with this benchmark, we find that our fine-to-coarse
algorithm registers long RGB-D sequences better than previous methods
- …