34 research outputs found
Visual and Camera Sensors
This book includes 13 papers published in Special Issue ("Visual and Camera Sensors") of the journal Sensors. The goal of this Special Issue was to invite high-quality, state-of-the-art research papers dealing with challenging issues in visual and camera sensors
Loop-closure detection by LiDAR scan re-identification
In this work, loop-closure detection from LiDAR scans is defined as an image re-identification problem. Re-identification is performed by computing Euclidean distances of a query scan to a gallery set of previous scans. The distances are computed in a feature embedding space where the scans are mapped by a convolutional neural network (CNN). The network is trained using the triplet loss training strategy. In our experiments we compare different backbone networks, variants of the triplet loss and generic and LiDAR specific data augmentation techniques. With a realistic indoor dataset the best architecture obtains the mean average precision (mAP) above 0.94.acceptedVersionPeer reviewe
HDPV-SLAM: Hybrid Depth-augmented Panoramic Visual SLAM for Mobile Mapping System with Tilted LiDAR and Panoramic Visual Camera
This paper proposes a novel visual simultaneous localization and mapping
(SLAM) system called Hybrid Depth-augmented Panoramic Visual SLAM (HDPV-SLAM),
that employs a panoramic camera and a tilted multi-beam LiDAR scanner to
generate accurate and metrically-scaled trajectories. RGB-D SLAM was the design
basis for HDPV-SLAM, which added depth information to visual features. It aims
to solve the two major issues hindering the performance of similar SLAM
systems. The first obstacle is the sparseness of LiDAR depth, which makes it
difficult to correlate it with the extracted visual features of the RGB image.
A deep learning-based depth estimation module for iteratively densifying sparse
LiDAR depth was suggested to address this issue. The second issue pertains to
the difficulties in depth association caused by a lack of horizontal overlap
between the panoramic camera and the tilted LiDAR sensor. To surmount this
difficulty, we present a hybrid depth association module that optimally
combines depth information estimated by two independent procedures,
feature-based triangulation and depth estimation. During a phase of feature
tracking, this hybrid depth association module aims to maximize the use of more
accurate depth information between the triangulated depth with visual features
tracked and the deep learning-based corrected depth. We evaluated the efficacy
of HDPV-SLAM using the 18.95 km-long York University and Teledyne Optech (YUTO)
MMS dataset. The experimental results demonstrate that the two proposed modules
contribute substantially to the performance of HDPV-SLAM, which surpasses that
of the state-of-the-art (SOTA) SLAM systems.Comment: 8 pages, 3 figures, To be published in IEEE International Conference
on Automation Science and Engineering (CASE) 202
The Revisiting Problem in Simultaneous Localization and Mapping: A Survey on Visual Loop Closure Detection
Where am I? This is one of the most critical questions that any intelligent
system should answer to decide whether it navigates to a previously visited
area. This problem has long been acknowledged for its challenging nature in
simultaneous localization and mapping (SLAM), wherein the robot needs to
correctly associate the incoming sensory data to the database allowing
consistent map generation. The significant advances in computer vision achieved
over the last 20 years, the increased computational power, and the growing
demand for long-term exploration contributed to efficiently performing such a
complex task with inexpensive perception sensors. In this article, visual loop
closure detection, which formulates a solution based solely on appearance input
data, is surveyed. We start by briefly introducing place recognition and SLAM
concepts in robotics. Then, we describe a loop closure detection system's
structure, covering an extensive collection of topics, including the feature
extraction, the environment representation, the decision-making step, and the
evaluation process. We conclude by discussing open and new research challenges,
particularly concerning the robustness in dynamic environments, the
computational complexity, and scalability in long-term operations. The article
aims to serve as a tutorial and a position paper for newcomers to visual loop
closure detection.Comment: 25 pages, 15 figure