Kavli Affiliate: Wei Gao
| First 5 Authors: Wei Gao, Wei Gao, , ,
| Summary:
Conventionally, human intuition often defines vision as a modality of passive
optical sensing, while active optical sensing is typically regarded as
measuring rather than the default modality of vision. However, the situation
now changes: sensor technologies and data-driven paradigms empower active
optical sensing to redefine the boundaries of vision, ushering in a new era of
active vision. Light Detection and Ranging (LiDAR) sensors capture reflectance
from object surfaces, which remains invariant under varying illumination
conditions, showcasing significant potential in robotic perception tasks such
as detection, recognition, segmentation, and Simultaneous Localization and
Mapping (SLAM). These applications often rely on dense sensing capabilities,
typically achieved by high-resolution, expensive LiDAR sensors. A key challenge
with low-cost LiDARs lies in the sparsity of scan data, which limits their
broader application. To address this limitation, this work introduces an
innovative framework for generating dense LiDAR reflectance images from sparse
data, leveraging the unique attributes of non-repeating scanning LiDAR
(NRS-LiDAR). We tackle critical challenges, including reflectance calibration
and the transition from static to dynamic scene domains, facilitating the
reconstruction of dense reflectance images in real-world settings. The key
contributions of this work include a comprehensive dataset for LiDAR
reflectance image densification, a densification network tailored for
NRS-LiDAR, and diverse applications such as loop closure and traffic lane
detection using the generated dense reflectance images.
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