24 research outputs found
Distributed bundle adjustment with block-based sparse matrix compression for super large scale datasets
We propose a distributed bundle adjustment (DBA) method using the exact
Levenberg-Marquardt (LM) algorithm for super large-scale datasets. Most of the
existing methods partition the global map to small ones and conduct bundle
adjustment in the submaps. In order to fit the parallel framework, they use
approximate solutions instead of the LM algorithm. However, those methods often
give sub-optimal results. Different from them, we utilize the exact LM
algorithm to conduct global bundle adjustment where the formation of the
reduced camera system (RCS) is actually parallelized and executed in a
distributed way. To store the large RCS, we compress it with a block-based
sparse matrix compression format (BSMC), which fully exploits its block
feature. The BSMC format also enables the distributed storage and updating of
the global RCS. The proposed method is extensively evaluated and compared with
the state-of-the-art pipelines using both synthetic and real datasets.
Preliminary results demonstrate the efficient memory usage and vast scalability
of the proposed method compared with the baselines. For the first time, we
conducted parallel bundle adjustment using LM algorithm on a real datasets with
1.18 million images and a synthetic dataset with 10 million images (about 500
times that of the state-of-the-art LM-based BA) on a distributed computing
system.Comment: camera ready version for ICCV202
PI-BA Bundle Adjustment Acceleration on Embedded FPGAs with Co-observation Optimization
Bundle adjustment (BA) is a fundamental optimization technique used in many
crucial applications, including 3D scene reconstruction, robotic localization,
camera calibration, autonomous driving, space exploration, street view map
generation etc. Essentially, BA is a joint non-linear optimization problem, and
one which can consume a significant amount of time and power, especially for
large optimization problems. Previous approaches of optimizing BA performance
heavily rely on parallel processing or distributed computing, which trade
higher power consumption for higher performance. In this paper we propose
{\pi}-BA, the first hardware-software co-designed BA engine on an embedded
FPGA-SoC that exploits custom hardware for higher performance and power
efficiency. Specifically, based on our key observation that not all points
appear on all images in a BA problem, we designed and implemented a
Co-Observation Optimization technique to accelerate BA operations with
optimized usage of memory and computation resources. Experimental results
confirm that {\pi}-BA outperforms the existing software implementations in
terms of performance and power consumption.Comment: in Proceedings of IEEE FCCM 201
Stochastic Bundle Adjustment for Efficient and Scalable 3D Reconstruction
Current bundle adjustment solvers such as the Levenberg-Marquardt (LM)
algorithm are limited by the bottleneck in solving the Reduced Camera System
(RCS) whose dimension is proportional to the camera number. When the problem is
scaled up, this step is neither efficient in computation nor manageable for a
single compute node. In this work, we propose a stochastic bundle adjustment
algorithm which seeks to decompose the RCS approximately inside the LM
iterations to improve the efficiency and scalability. It first reformulates the
quadratic programming problem of an LM iteration based on the clustering of the
visibility graph by introducing the equality constraints across clusters. Then,
we propose to relax it into a chance constrained problem and solve it through
sampled convex program. The relaxation is intended to eliminate the
interdependence between clusters embodied by the constraints, so that a large
RCS can be decomposed into independent linear sub-problems. Numerical
experiments on unordered Internet image sets and sequential SLAM image sets, as
well as distributed experiments on large-scale datasets, have demonstrated the
high efficiency and scalability of the proposed approach. Codes are released at
https://github.com/zlthinker/STBA.Comment: Accepted by ECCV 202
SLAM: Decentralized and Distributed Collaborative Visual-inertial SLAM System for Aerial Swarm
In recent years, aerial swarm technology has developed rapidly. In order to
accomplish a fully autonomous aerial swarm, a key technology is decentralized
and distributed collaborative SLAM (CSLAM) for aerial swarms, which estimates
the relative pose and the consistent global trajectories. In this paper, we
propose SLAM: a decentralized and distributed () collaborative SLAM
algorithm. This algorithm has high local accuracy and global consistency, and
the distributed architecture allows it to scale up. SLAM covers swarm
state estimation in two scenarios: near-field state estimation for high
real-time accuracy at close range and far-field state estimation for globally
consistent trajectories estimation at the long-range between UAVs. Distributed
optimization algorithms are adopted as the backend to achieve the goal.
SLAM is robust to transient loss of communication, network delays, and
other factors. Thanks to the flexible architecture, SLAM has the potential
of applying in various scenarios
Photometric LiDAR and RGB-D Bundle Adjustment
The joint optimization of the sensor trajectory and 3D map is a crucial
characteristic of Simultaneous Localization and Mapping (SLAM) systems. To
achieve this, the gold standard is Bundle Adjustment (BA). Modern 3D LiDARs now
retain higher resolutions that enable the creation of point cloud images
resembling those taken by conventional cameras. Nevertheless, the typical
effective global refinement techniques employed for RGB-D sensors are not
widely applied to LiDARs. This paper presents a novel BA photometric strategy
that accounts for both RGB-D and LiDAR in the same way. Our work can be used on
top of any SLAM/GNSS estimate to improve and refine the initial trajectory. We
conducted different experiments using these two depth sensors on public
benchmarks. Our results show that our system performs on par or better compared
to other state-of-the-art ad-hoc SLAM/BA strategies, free from data association
and without making assumptions about the environment. In addition, we present
the benefit of jointly using RGB-D and LiDAR within our unified method. We
finally release an open-source CUDA/C++ implementation.Comment: 11 pages, 9 figure
Distributed Multi-agent Video Fast-forwarding
In many intelligent systems, a network of agents collaboratively perceives
the environment for better and more efficient situation awareness. As these
agents often have limited resources, it could be greatly beneficial to identify
the content overlapping among camera views from different agents and leverage
it for reducing the processing, transmission and storage of
redundant/unimportant video frames. This paper presents a consensus-based
distributed multi-agent video fast-forwarding framework, named DMVF, that
fast-forwards multi-view video streams collaboratively and adaptively. In our
framework, each camera view is addressed by a reinforcement learning based
fast-forwarding agent, which periodically chooses from multiple strategies to
selectively process video frames and transmits the selected frames at
adjustable paces. During every adaptation period, each agent communicates with
a number of neighboring agents, evaluates the importance of the selected frames
from itself and those from its neighbors, refines such evaluation together with
other agents via a system-wide consensus algorithm, and uses such evaluation to
decide their strategy for the next period. Compared with approaches in the
literature on a real-world surveillance video dataset VideoWeb, our method
significantly improves the coverage of important frames and also reduces the
number of frames processed in the system.Comment: To appear at ACM Multimedia 202
3D city scale reconstruction using wide area motion imagery
3D reconstruction is one of the most challenging but also most necessary part of computer vision. It is generally applied everywhere, from remote sensing to medical imaging and multimedia. Wide Area Motion Imagery is a field that has gained traction over the recent years. It consists in using an airborne large field of view sensor to cover a typically over a square kilometer area for each captured image. This is particularly valuable data for analysis but the amount of information is overwhelming for any human analyst. Algorithms to efficiently and automatically extract information are therefore needed and 3D reconstruction plays a critical part in it, along with detection and tracking. This dissertation work presents novel reconstruction algorithms to compute a 3D probabilistic space, a set of experiments to efficiently extract photo realistic 3D point clouds and a range of transformations for possible applications of the generated 3D data to filtering, data compression and mapping. The algorithms have been successfully tested on our own datasets provided by Transparent Sky and this thesis work also proposes methods to evaluate accuracy, completeness and photo-consistency. The generated data has been successfully used to improve detection and tracking performances, and allows data compression and extrapolation by generating synthetic images from new point of view, and data augmentation with the inferred occlusion areas.Includes bibliographical reference