27 research outputs found
Incremental Dense Reconstruction from Monocular Video with Guided Sparse Feature Volume Fusion
Incrementally recovering 3D dense structures from monocular videos is of
paramount importance since it enables various robotics and AR applications.
Feature volumes have recently been shown to enable efficient and accurate
incremental dense reconstruction without the need to first estimate depth, but
they are not able to achieve as high of a resolution as depth-based methods due
to the large memory consumption of high-resolution feature volumes. This letter
proposes a real-time feature volume-based dense reconstruction method that
predicts TSDF (Truncated Signed Distance Function) values from a novel
sparsified deep feature volume, which is able to achieve higher resolutions
than previous feature volume-based methods, and is favorable in large-scale
outdoor scenarios where the majority of voxels are empty. An uncertainty-aware
multi-view stereo (MVS) network is leveraged to infer initial voxel locations
of the physical surface in a sparse feature volume. Then for refining the
recovered 3D geometry, deep features are attentively aggregated from multiview
images at potential surface locations, and temporally fused. Besides achieving
higher resolutions than before, our method is shown to produce more complete
reconstructions with finer detail in many cases. Extensive evaluations on both
public and self-collected datasets demonstrate a very competitive real-time
reconstruction result for our method compared to state-of-the-art
reconstruction methods in both indoor and outdoor settings.Comment: 8 pages, 5 figures, RA-L 202
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Risk Response for Municipal Solid Waste Crisis Using Ontology-Based Reasoning
Many cities in the world are besieged by municipal solid waste (MSW). MSW not only pollutes the ecological environment but can even induce a series of public safety crises. Risk response for MSW needs novel changes. This paper innovatively adopts the ideas and methods of semantic web ontology to build an ontology-based reasoning system for MSW risk response. Through the integration of crisis information and case resources in the field of MSW, combined with the reasoning ability of Semantic Web Rule Language (SWRL), a system of rule reasoning for risk transformation is constructed. Knowledge extraction and integration of MSW risk response can effectively excavate semantic correlation of crisis information along with key transformation points in the process of crisis evolution through rule reasoning. The results show that rule reasoning of transformation can effectively improve intelligent decision-making regarding MSW risk response
Kinematics Based Visual Localization for Skid-Steering Robots: Algorithm and Theory
To build commercial robots, skid-steering mechanical design is of increased
popularity due to its manufacturing simplicity and unique mechanism. However,
these also cause significant challenges on software and algorithm design,
especially for pose estimation (i.e., determining the robot's rotation and
position), which is the prerequisite of autonomous navigation. While the
general localization algorithms have been extensively studied in research
communities, there are still fundamental problems that need to be resolved for
localizing skid-steering robots that change their orientation with a skid. To
tackle this problem, we propose a probabilistic sliding-window estimator
dedicated to skid-steering robots, using measurements from a monocular camera,
the wheel encoders, and optionally an inertial measurement unit (IMU).
Specifically, we explicitly model the kinematics of skid-steering robots by
both track instantaneous centers of rotation (ICRs) and correction factors,
which are capable of compensating for the complexity of track-to-terrain
interaction, the imperfectness of mechanical design, terrain conditions and
smoothness, and so on. To prevent performance reduction in robots' lifelong
missions, the time- and location- varying kinematic parameters are estimated
online along with pose estimation states in a tightly-coupled manner. More
importantly, we conduct in-depth observability analysis for different sensors
and design configurations in this paper, which provides us with theoretical
tools in making the correct choice when building real commercial robots. In our
experiments, we validate the proposed method by both simulation tests and
real-world experiments, which demonstrate that our method outperforms competing
methods by wide margins.Comment: 18 pages in tota
Continuous-Time Fixed-Lag Smoothing for LiDAR-Inertial-Camera SLAM
Localization and mapping with heterogeneous multi-sensor fusion have been
prevalent in recent years. To adequately fuse multi-modal sensor measurements
received at different time instants and different frequencies, we estimate the
continuous-time trajectory by fixed-lag smoothing within a factor-graph
optimization framework. With the continuous-time formulation, we can query
poses at any time instants corresponding to the sensor measurements. To bound
the computation complexity of the continuous-time fixed-lag smoother, we
maintain temporal and keyframe sliding windows with constant size, and
probabilistically marginalize out control points of the trajectory and other
states, which allows preserving prior information for future sliding-window
optimization. Based on continuous-time fixed-lag smoothing, we design
tightly-coupled multi-modal SLAM algorithms with a variety of sensor
combinations, like the LiDAR-inertial and LiDAR-inertial-camera SLAM systems,
in which online timeoffset calibration is also naturally supported. More
importantly, benefiting from the marginalization and our derived analytical
Jacobians for optimization, the proposed continuous-time SLAM systems can
achieve real-time performance regardless of the high complexity of
continuous-time formulation. The proposed multi-modal SLAM systems have been
widely evaluated on three public datasets and self-collect datasets. The
results demonstrate that the proposed continuous-time SLAM systems can achieve
high-accuracy pose estimations and outperform existing state-of-the-art
methods. To benefit the research community, we will open source our code at
~\url{https://github.com/APRIL-ZJU/clic}
CodeVIO: Visual-Inertial Odometry with Learned Optimizable Dense Depth
In this work, we present a lightweight, tightly-coupled deep depth network
and visual-inertial odometry (VIO) system, which can provide accurate state
estimates and dense depth maps of the immediate surroundings. Leveraging the
proposed lightweight Conditional Variational Autoencoder (CVAE) for depth
inference and encoding, we provide the network with previously marginalized
sparse features from VIO to increase the accuracy of initial depth prediction
and generalization capability. The compact encoded depth maps are then updated
jointly with navigation states in a sliding window estimator in order to
provide the dense local scene geometry. We additionally propose a novel method
to obtain the CVAE's Jacobian which is shown to be more than an order of
magnitude faster than previous works, and we additionally leverage
First-Estimate Jacobian (FEJ) to avoid recalculation. As opposed to previous
works relying on completely dense residuals, we propose to only provide sparse
measurements to update the depth code and show through careful experimentation
that our choice of sparse measurements and FEJs can still significantly improve
the estimated depth maps. Our full system also exhibits state-of-the-art pose
estimation accuracy, and we show that it can run in real-time with
single-thread execution while utilizing GPU acceleration only for the network
and code Jacobian.Comment: 6 Figure
The Effect of Anesthesia on the Immune System in Colorectal Cancer Patients
Colorectal cancer (CRC) is the key leading cause of high morbidity and mortality worldwide. Surgical excision is the most effective treatment for CRC. However, stress caused by surgery response can destroy the body’s immunity and increase the likelihood of cancer dissemination and metastasis. Anesthesia is an effective way to control the stress response, and recent basic and clinical research has shown that anesthesia and related drugs can directly or indirectly affect the immune system of colorectal cancer patients during the perioperative period. Thus, these drugs may affect the prognosis of CRC surgery patients. This review is intended to summarize currently available data regarding the effects of anesthetics and related drugs on perioperative immune function and postoperative recurrence and metastasis in CRC patients. Determining the most suitable anesthesia for patients with CRC is of utmost importance