2,288 research outputs found
A ROBUST RGB-D SLAM SYSTEM FOR 3D ENVIRONMENT WITH PLANAR SURFACES
Simultaneous localization and mapping is the technique to construct a 3D map of unknown environment. With the increasing popularity of RGB-depth (RGB-D) sensors such as the Microsoft Kinect, there have been much research on capturing and reconstructing 3D environments using a movable RGB-D sensor. The key process behind these kinds of simultaneous location and mapping (SLAM) systems is the iterative closest point or ICP algorithm, which is an iterative algorithm that can estimate the rigid movement of the camera based on the captured 3D point clouds. While ICP is a well-studied algorithm, it is problematic when it is used in scanning large planar regions such as wall surfaces in a room. The lack of depth variations on planar surfaces makes the global alignment an ill-conditioned problem. In this thesis, we present a novel approach for registering 3D point clouds by combining both color and depth information. Instead of directly searching for point correspondences among 3D data, the proposed method first extracts features from the RGB images, and then back-projects the features to the 3D space to identify more reliable correspondences. These color correspondences form the initial input to the ICP procedure which then proceeds to refine the alignment. Experimental results show that our proposed approach can achieve better accuracy than existing SLAMs in reconstructing indoor environments with large planar surfaces
Dense Piecewise Planar RGB-D SLAM for Indoor Environments
The paper exploits weak Manhattan constraints to parse the structure of
indoor environments from RGB-D video sequences in an online setting. We extend
the previous approach for single view parsing of indoor scenes to video
sequences and formulate the problem of recovering the floor plan of the
environment as an optimal labeling problem solved using dynamic programming.
The temporal continuity is enforced in a recursive setting, where labeling from
previous frames is used as a prior term in the objective function. In addition
to recovery of piecewise planar weak Manhattan structure of the extended
environment, the orthogonality constraints are also exploited by visual
odometry and pose graph optimization. This yields reliable estimates in the
presence of large motions and absence of distinctive features to track. We
evaluate our method on several challenging indoors sequences demonstrating
accurate SLAM and dense mapping of low texture environments. On existing TUM
benchmark we achieve competitive results with the alternative approaches which
fail in our environments.Comment: International Conference on Intelligent Robots and Systems (IROS)
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
Probabilistic Combination of Noisy Points and Planes for RGB-D Odometry
This work proposes a visual odometry method that combines points and plane
primitives, extracted from a noisy depth camera. Depth measurement uncertainty
is modelled and propagated through the extraction of geometric primitives to
the frame-to-frame motion estimation, where pose is optimized by weighting the
residuals of 3D point and planes matches, according to their uncertainties.
Results on an RGB-D dataset show that the combination of points and planes,
through the proposed method, is able to perform well in poorly textured
environments, where point-based odometry is bound to fail.Comment: Accepted to TAROS 201
Plane-Based Optimization of Geometry and Texture for RGB-D Reconstruction of Indoor Scenes
We present a novel approach to reconstruct RGB-D indoor scene with plane
primitives. Our approach takes as input a RGB-D sequence and a dense coarse
mesh reconstructed by some 3D reconstruction method on the sequence, and
generate a lightweight, low-polygonal mesh with clear face textures and sharp
features without losing geometry details from the original scene. To achieve
this, we firstly partition the input mesh with plane primitives, simplify it
into a lightweight mesh next, then optimize plane parameters, camera poses and
texture colors to maximize the photometric consistency across frames, and
finally optimize mesh geometry to maximize consistency between geometry and
planes. Compared to existing planar reconstruction methods which only cover
large planar regions in the scene, our method builds the entire scene by
adaptive planes without losing geometry details and preserves sharp features in
the final mesh. We demonstrate the effectiveness of our approach by applying it
onto several RGB-D scans and comparing it to other state-of-the-art
reconstruction methods.Comment: in International Conference on 3D Vision 2018; Models and Code: see
https://github.com/chaowang15/plane-opt-rgbd. arXiv admin note: text overlap
with arXiv:1905.0885
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