2,167 research outputs found
3D Model-free Visual Localization System from Essential Matrix under Local Planar Motion
Visual localization plays a critical role in the functionality of low-cost
autonomous mobile robots. Current state-of-the-art approaches for achieving
accurate visual localization are 3D scene-specific, requiring additional
computational and storage resources to construct a 3D scene model when facing a
new environment. An alternative approach of directly using a database of 2D
images for visual localization offers more flexibility. However, such methods
currently suffer from limited localization accuracy. In this paper, we propose
an accurate and robust multiple checking-based 3D model-free visual
localization system to address the aforementioned issues. To ensure high
accuracy, our focus is on estimating the pose of a query image relative to the
retrieved database images using 2D-2D feature matches. Theoretically, by
incorporating the local planar motion constraint into both the estimation of
the essential matrix and the triangulation stages, we reduce the minimum
required feature matches for absolute pose estimation, thereby enhancing the
robustness of outlier rejection. Additionally, we introduce a multiple-checking
mechanism to ensure the correctness of the solution throughout the solving
process. For validation, qualitative and quantitative experiments are performed
on both simulation and two real-world datasets and the experimental results
demonstrate a significant enhancement in both accuracy and robustness afforded
by the proposed 3D model-free visual localization system
Three-Dimensional Culture of Hybridoma Cells Secreting Anti-Human Chorionic Gonadotropin by a New Rolling Culture System
Cell growth rate and production of monoclonal antibody (MAb) of hybridoma cells producing anti-human chorionic gonadotropin (hCG) MAb have been used as investigation criteria in double-mouthed rolling bottle (DMRB). Compared with T-flask cell culture, both of the cell number and MAb production increased by approximately 42.5% when the medium was supplemented with 5% fetal calf serum (FCS) and DMRB rotated at 2 turns per minute. Yield of MAb was experimentally related to the number of viable cells. Interestingly, MAb yield was four times as high as that cultured in T-flask in the first 24 hours, and about 75% yield of total MAb was secreted by 48 hours during the culture. It appears that the promoted cell growth and MAb yield are resulted from the three-dimensional growth of hybridoma cells under a suitably revolving condition
Leveraging BEV Representation for 360-degree Visual Place Recognition
This paper investigates the advantages of using Bird's Eye View (BEV)
representation in 360-degree visual place recognition (VPR). We propose a novel
network architecture that utilizes the BEV representation in feature
extraction, feature aggregation, and vision-LiDAR fusion, which bridges visual
cues and spatial awareness. Our method extracts image features using standard
convolutional networks and combines the features according to pre-defined 3D
grid spatial points. To alleviate the mechanical and time misalignments between
cameras, we further introduce deformable attention to learn the compensation.
Upon the BEV feature representation, we then employ the polar transform and the
Discrete Fourier transform for aggregation, which is shown to be
rotation-invariant. In addition, the image and point cloud cues can be easily
stated in the same coordinates, which benefits sensor fusion for place
recognition. The proposed BEV-based method is evaluated in ablation and
comparative studies on two datasets, including on-the-road and off-the-road
scenarios. The experimental results verify the hypothesis that BEV can benefit
VPR by its superior performance compared to baseline methods. To the best of
our knowledge, this is the first trial of employing BEV representation in this
task
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