1,820 research outputs found
LDSO: Direct Sparse Odometry with Loop Closure
In this paper we present an extension of Direct Sparse Odometry (DSO) to a
monocular visual SLAM system with loop closure detection and pose-graph
optimization (LDSO). As a direct technique, DSO can utilize any image pixel
with sufficient intensity gradient, which makes it robust even in featureless
areas. LDSO retains this robustness, while at the same time ensuring
repeatability of some of these points by favoring corner features in the
tracking frontend. This repeatability allows to reliably detect loop closure
candidates with a conventional feature-based bag-of-words (BoW) approach. Loop
closure candidates are verified geometrically and Sim(3) relative pose
constraints are estimated by jointly minimizing 2D and 3D geometric error
terms. These constraints are fused with a co-visibility graph of relative poses
extracted from DSO's sliding window optimization. Our evaluation on publicly
available datasets demonstrates that the modified point selection strategy
retains the tracking accuracy and robustness, and the integrated pose-graph
optimization significantly reduces the accumulated rotation-, translation- and
scale-drift, resulting in an overall performance comparable to state-of-the-art
feature-based systems, even without global bundle adjustment
GSLAM: Initialization-robust Monocular Visual SLAM via Global Structure-from-Motion
Many monocular visual SLAM algorithms are derived from incremental
structure-from-motion (SfM) methods. This work proposes a novel monocular SLAM
method which integrates recent advances made in global SfM. In particular, we
present two main contributions to visual SLAM. First, we solve the visual
odometry problem by a novel rank-1 matrix factorization technique which is more
robust to the errors in map initialization. Second, we adopt a recent global
SfM method for the pose-graph optimization, which leads to a multi-stage linear
formulation and enables L1 optimization for better robustness to false loops.
The combination of these two approaches generates more robust reconstruction
and is significantly faster (4X) than recent state-of-the-art SLAM systems. We
also present a new dataset recorded with ground truth camera motion in a Vicon
motion capture room, and compare our method to prior systems on it and
established benchmark datasets.Comment: 3DV 2017 Project Page: https://frobelbest.github.io/gsla
Fast, Accurate Thin-Structure Obstacle Detection for Autonomous Mobile Robots
Safety is paramount for mobile robotic platforms such as self-driving cars
and unmanned aerial vehicles. This work is devoted to a task that is
indispensable for safety yet was largely overlooked in the past -- detecting
obstacles that are of very thin structures, such as wires, cables and tree
branches. This is a challenging problem, as thin objects can be problematic for
active sensors such as lidar and sonar and even for stereo cameras. In this
work, we propose to use video sequences for thin obstacle detection. We
represent obstacles with edges in the video frames, and reconstruct them in 3D
using efficient edge-based visual odometry techniques. We provide both a
monocular camera solution and a stereo camera solution. The former incorporates
Inertial Measurement Unit (IMU) data to solve scale ambiguity, while the latter
enjoys a novel, purely vision-based solution. Experiments demonstrated that the
proposed methods are fast and able to detect thin obstacles robustly and
accurately under various conditions.Comment: Appeared at IEEE CVPR 2017 Workshop on Embedded Visio
UcoSLAM: Simultaneous Localization and Mapping by Fusion of KeyPoints and Squared Planar Markers
This paper proposes a novel approach for Simultaneous Localization and
Mapping by fusing natural and artificial landmarks. Most of the SLAM approaches
use natural landmarks (such as keypoints). However, they are unstable over
time, repetitive in many cases or insufficient for a robust tracking (e.g. in
indoor buildings). On the other hand, other approaches have employed artificial
landmarks (such as squared fiducial markers) placed in the environment to help
tracking and relocalization. We propose a method that integrates both
approaches in order to achieve long-term robust tracking in many scenarios.
Our method has been compared to the start-of-the-art methods ORB-SLAM2 and
LDSO in the public dataset Kitti, Euroc-MAV, TUM and SPM, obtaining better
precision, robustness and speed. Our tests also show that the combination of
markers and keypoints achieves better accuracy than each one of them
independently.Comment: Paper submitted to Pattern Recognitio
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