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Image Understanding and Robotics Research at Columbia University
Over the past year, the research investigations of the Vision/Robotics Laboratory at Columbia University have reflected the interests of its four faculty members, two staff programmers, and 16 Ph.D. students. Several of the projects involve other faculty members in the department or the university, or researchers at AT&T, IBM, or Philips. We list below a summary of our interests and results, together with the principal researchers associated with them. Since it is difficult to separate those aspects of robotic research that are purely visual from those that are vision-like (for example, tactile sensing) or vision-related (for example, integrated vision-robotic systems), we have listed all robotic research that is not purely manipulative. The majority of our current investigations are deepenings of work reported last year; this was the second year of both our basic Image Understanding contract and our Strategic Computing contract. Therefore, the form of this year's report closely resembles last year's. Although there are a few new initiatives, mainly we report the new results we have obtained in the same five basic research areas. Much of this work is summarized on a video tape that is available on request. We also note two service contributions this past year. The Special Issue on Computer Vision of the Proceedings of the IEEE, August, 1988, was co-edited by one of us (John Kender [27]). And, the upcoming IEEE Computer Society Conference on Computer Vision and Pattem Recognition, June, 1989, is co-program chaired by one of us (John Kender [23])
Long-Term Urban Vehicle Localization Using Pole Landmarks Extracted from 3-D Lidar Scans
Due to their ubiquity and long-term stability, pole-like objects are well
suited to serve as landmarks for vehicle localization in urban environments. In
this work, we present a complete mapping and long-term localization system
based on pole landmarks extracted from 3-D lidar data. Our approach features a
novel pole detector, a mapping module, and an online localization module, each
of which are described in detail, and for which we provide an open-source
implementation at www.github.com/acschaefer/polex. In extensive experiments, we
demonstrate that our method improves on the state of the art with respect to
long-term reliability and accuracy: First, we prove reliability by tasking the
system with localizing a mobile robot over the course of 15~months in an urban
area based on an initial map, confronting it with constantly varying routes,
differing weather conditions, seasonal changes, and construction sites. Second,
we show that the proposed approach clearly outperforms a recently published
method in terms of accuracy.Comment: 9 page
MOZARD: Multi-Modal Localization for Autonomous Vehicles in Urban Outdoor Environments
Visually poor scenarios are one of the main sources of failure in visual
localization systems in outdoor environments. To address this challenge, we
present MOZARD, a multi-modal localization system for urban outdoor
environments using vision and LiDAR. By extending our preexisting key-point
based visual multi-session local localization approach with the use of semantic
data, an improved localization recall can be achieved across vastly different
appearance conditions. In particular we focus on the use of curbstone
information because of their broad distribution and reliability within urban
environments. We present thorough experimental evaluations on several driving
kilometers in challenging urban outdoor environments, analyze the recall and
accuracy of our localization system and demonstrate in a case study possible
failure cases of each subsystem. We demonstrate that MOZARD is able to bridge
scenarios where our previous work VIZARD fails, hence yielding an increased
recall performance, while a similar localization accuracy of 0.2m is achieve
SuperLine3D: Self-supervised Line Segmentation and Description for LiDAR Point Cloud
Poles and building edges are frequently observable objects on urban roads,
conveying reliable hints for various computer vision tasks. To repetitively
extract them as features and perform association between discrete LiDAR frames
for registration, we propose the first learning-based feature segmentation and
description model for 3D lines in LiDAR point cloud. To train our model without
the time consuming and tedious data labeling process, we first generate
synthetic primitives for the basic appearance of target lines, and build an
iterative line auto-labeling process to gradually refine line labels on real
LiDAR scans. Our segmentation model can extract lines under arbitrary scale
perturbations, and we use shared EdgeConv encoder layers to train the two
segmentation and descriptor heads jointly. Base on the model, we can build a
highly-available global registration module for point cloud registration, in
conditions without initial transformation hints. Experiments have demonstrated
that our line-based registration method is highly competitive to
state-of-the-art point-based approaches. Our code is available at
https://github.com/zxrzju/SuperLine3D.git.Comment: 17 pages, ECCV 2022 Accepte
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