234 research outputs found
MAVIS: Multi-Camera Augmented Visual-Inertial SLAM using SE2(3) Based Exact IMU Pre-integration
We present a novel optimization-based Visual-Inertial SLAM system designed
for multiple partially overlapped camera systems, named MAVIS. Our framework
fully exploits the benefits of wide field-of-view from multi-camera systems,
and the metric scale measurements provided by an inertial measurement unit
(IMU). We introduce an improved IMU pre-integration formulation based on the
exponential function of an automorphism of SE_2(3), which can effectively
enhance tracking performance under fast rotational motion and extended
integration time. Furthermore, we extend conventional front-end tracking and
back-end optimization module designed for monocular or stereo setup towards
multi-camera systems, and introduce implementation details that contribute to
the performance of our system in challenging scenarios. The practical validity
of our approach is supported by our experiments on public datasets. Our MAVIS
won the first place in all the vision-IMU tracks (single and multi-session
SLAM) on Hilti SLAM Challenge 2023 with 1.7 times the score compared to the
second place.Comment: video link: https://youtu.be/Q_jZSjhNFf
WeedMap: A large-scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming
We present a novel weed segmentation and mapping framework that processes
multispectral images obtained from an unmanned aerial vehicle (UAV) using a
deep neural network (DNN). Most studies on crop/weed semantic segmentation only
consider single images for processing and classification. Images taken by UAVs
often cover only a few hundred square meters with either color only or color
and near-infrared (NIR) channels. Computing a single large and accurate
vegetation map (e.g., crop/weed) using a DNN is non-trivial due to difficulties
arising from: (1) limited ground sample distances (GSDs) in high-altitude
datasets, (2) sacrificed resolution resulting from downsampling high-fidelity
images, and (3) multispectral image alignment. To address these issues, we
adopt a stand sliding window approach that operates on only small portions of
multispectral orthomosaic maps (tiles), which are channel-wise aligned and
calibrated radiometrically across the entire map. We define the tile size to be
the same as that of the DNN input to avoid resolution loss. Compared to our
baseline model (i.e., SegNet with 3 channel RGB inputs) yielding an area under
the curve (AUC) of [background=0.607, crop=0.681, weed=0.576], our proposed
model with 9 input channels achieves [0.839, 0.863, 0.782]. Additionally, we
provide an extensive analysis of 20 trained models, both qualitatively and
quantitatively, in order to evaluate the effects of varying input channels and
tunable network hyperparameters. Furthermore, we release a large sugar
beet/weed aerial dataset with expertly guided annotations for further research
in the fields of remote sensing, precision agriculture, and agricultural
robotics.Comment: 25 pages, 14 figures, MDPI Remote Sensin
- …