54 research outputs found
Leveraging Deep Visual Descriptors for Hierarchical Efficient Localization
Many robotics applications require precise pose estimates despite operating
in large and changing environments. This can be addressed by visual
localization, using a pre-computed 3D model of the surroundings. The pose
estimation then amounts to finding correspondences between 2D keypoints in a
query image and 3D points in the model using local descriptors. However,
computational power is often limited on robotic platforms, making this task
challenging in large-scale environments. Binary feature descriptors
significantly speed up this 2D-3D matching, and have become popular in the
robotics community, but also strongly impair the robustness to perceptual
aliasing and changes in viewpoint, illumination and scene structure. In this
work, we propose to leverage recent advances in deep learning to perform an
efficient hierarchical localization. We first localize at the map level using
learned image-wide global descriptors, and subsequently estimate a precise pose
from 2D-3D matches computed in the candidate places only. This restricts the
local search and thus allows to efficiently exploit powerful non-binary
descriptors usually dismissed on resource-constrained devices. Our approach
results in state-of-the-art localization performance while running in real-time
on a popular mobile platform, enabling new prospects for robotics research.Comment: CoRL 2018 Camera-ready (fix typos and update citations
Dynamic Objects Segmentation for Visual Localization in Urban Environments
Visual localization and mapping is a crucial capability to address many
challenges in mobile robotics. It constitutes a robust, accurate and
cost-effective approach for local and global pose estimation within prior maps.
Yet, in highly dynamic environments, like crowded city streets, problems arise
as major parts of the image can be covered by dynamic objects. Consequently,
visual odometry pipelines often diverge and the localization systems
malfunction as detected features are not consistent with the precomputed 3D
model. In this work, we present an approach to automatically detect dynamic
object instances to improve the robustness of vision-based localization and
mapping in crowded environments. By training a convolutional neural network
model with a combination of synthetic and real-world data, dynamic object
instance masks are learned in a semi-supervised way. The real-world data can be
collected with a standard camera and requires minimal further post-processing.
Our experiments show that a wide range of dynamic objects can be reliably
detected using the presented method. Promising performance is demonstrated on
our own and also publicly available datasets, which also shows the
generalization capabilities of this approach.Comment: 4 pages, submitted to the IROS 2018 Workshop "From Freezing to
Jostling Robots: Current Challenges and New Paradigms for Safe Robot
Navigation in Dense Crowds
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
Plug-and-Play SLAM: A Unified SLAM Architecture for Modularity and Ease of Use
Nowadays, SLAM (Simultaneous Localization and Mapping) is considered by the
Robotics community to be a mature field. Currently, there are many open-source
systems that are able to deliver fast and accurate estimation in typical
real-world scenarios. Still, all these systems often provide an ad-hoc
implementation that entailed to predefined sensor configurations. In this work,
we tackle this issue, proposing a novel SLAM architecture specifically designed
to address heterogeneous sensors' configuration and to standardize SLAM
solutions. Thanks to its modularity and to specific design patterns, the
presented architecture is easy to extend, enhancing code reuse and efficiency.
Finally, adopting our solution, we conducted comparative experiments for a
variety of sensor configurations, showing competitive results that confirm
state-of-the-art performance
Multi-Session, Localization-oriented and Lightweight LiDAR Mapping Using Semantic Lines and Planes
In this paper, we present a centralized framework for multi-session LiDAR
mapping in urban environments, by utilizing lightweight line and plane map
representations instead of widely used point clouds. The proposed framework
achieves consistent mapping in a coarse-to-fine manner. Global place
recognition is achieved by associating lines and planes on the Grassmannian
manifold, followed by an outlier rejection-aided pose graph optimization for
map merging. Then a novel bundle adjustment is also designed to improve the
local consistency of lines and planes. In the experimental section, both public
and self-collected datasets are used to demonstrate efficiency and
effectiveness. Extensive results validate that our LiDAR mapping framework
could merge multi-session maps globally, optimize maps incrementally, and is
applicable for lightweight robot localization.Comment: Accepted by IROS202
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