1,850 research outputs found
Consistent ICP for the registration of sparse and inhomogeneous point clouds
In this paper, we derive a novel iterative closest point (ICP) technique that performs point cloud alignment in a robust and consistent way. Traditional ICP techniques minimize the point-to-point distances, which are successful when point clouds contain no noise or clutter and moreover are dense and more or less uniformly sampled. In the other case, it is better to employ point-to-plane or other metrics to locally approximate the surface of the objects. However, the point-to-plane metric does not yield a symmetric solution, i.e. the estimated transformation of point cloud p to point cloud q is not necessarily equal to the inverse transformation of point cloud q to point cloud p. In order to improve ICP, we will enforce such symmetry constraints as prior knowledge and make it also robust to noise and clutter. Experimental results show that our method is indeed much more consistent and accurate in presence of noise and clutter compared to existing ICP algorithms
X-ICP: Localizability-Aware LiDAR Registration for Robust Localization in Extreme Environments
Modern robotic systems are required to operate in challenging environments,
which demand reliable localization under challenging conditions. LiDAR-based
localization methods, such as the Iterative Closest Point (ICP) algorithm, can
suffer in geometrically uninformative environments that are known to
deteriorate point cloud registration performance and push optimization toward
divergence along weakly constrained directions. To overcome this issue, this
work proposes i) a robust fine-grained localizability detection module, and ii)
a localizability-aware constrained ICP optimization module, which couples with
the localizability detection module in a unified manner. The proposed
localizability detection is achieved by utilizing the correspondences between
the scan and the map to analyze the alignment strength against the principal
directions of the optimization as part of its fine-grained LiDAR localizability
analysis. In the second part, this localizability analysis is then integrated
into the scan-to-map point cloud registration to generate drift-free pose
updates by enforcing controlled updates or leaving the degenerate directions of
the optimization unchanged. The proposed method is thoroughly evaluated and
compared to state-of-the-art methods in simulated and real-world experiments,
demonstrating the performance and reliability improvement in LiDAR-challenging
environments. In all experiments, the proposed framework demonstrates accurate
and generalizable localizability detection and robust pose estimation without
environment-specific parameter tuning.Comment: 20 Pages, 20 Figures Submitted to IEEE Transactions On Robotics.
Supplementary Video: https://youtu.be/SviLl7q69aA Project Website:
https://sites.google.com/leggedrobotics.com/x-ic
Robust Photogeometric Localization over Time for Map-Centric Loop Closure
Map-centric SLAM is emerging as an alternative of conventional graph-based
SLAM for its accuracy and efficiency in long-term mapping problems. However, in
map-centric SLAM, the process of loop closure differs from that of conventional
SLAM and the result of incorrect loop closure is more destructive and is not
reversible. In this paper, we present a tightly coupled photogeometric metric
localization for the loop closure problem in map-centric SLAM. In particular,
our method combines complementary constraints from LiDAR and camera sensors,
and validates loop closure candidates with sequential observations. The
proposed method provides a visual evidence-based outlier rejection where
failures caused by either place recognition or localization outliers can be
effectively removed. We demonstrate the proposed method is not only more
accurate than the conventional global ICP methods but is also robust to
incorrect initial pose guesses.Comment: To Appear in IEEE ROBOTICS AND AUTOMATION LETTERS, ACCEPTED JANUARY
201
NOVEL DENSE STEREO ALGORITHMS FOR HIGH-QUALITY DEPTH ESTIMATION FROM IMAGES
This dissertation addresses the problem of inferring scene depth information from a collection of calibrated images taken from different viewpoints via stereo matching. Although it has been heavily investigated for decades, depth from stereo remains a long-standing challenge and popular research topic for several reasons. First of all, in order to be of practical use for many real-time applications such as autonomous driving, accurate depth estimation in real-time is of great importance and one of the core challenges in stereo. Second, for applications such as 3D reconstruction and view synthesis, high-quality depth estimation is crucial to achieve photo realistic results. However, due to the matching ambiguities, accurate dense depth estimates are difficult to achieve. Last but not least, most stereo algorithms rely on identification of corresponding points among images and only work effectively when scenes are Lambertian. For non-Lambertian surfaces, the brightness constancy assumption is no longer valid. This dissertation contributes three novel stereo algorithms that are motivated by the specific requirements and limitations imposed by different applications.
In addressing high speed depth estimation from images, we present a stereo algorithm that achieves high quality results while maintaining real-time performance. We introduce an adaptive aggregation step in a dynamic-programming framework. Matching costs are aggregated in the vertical direction using a computationally expensive weighting scheme based on color and distance proximity. We utilize the vector processing capability and parallelism in commodity graphics hardware to speed up this process over two orders of magnitude.
In addressing high accuracy depth estimation, we present a stereo model that makes use of constraints from points with known depths - the Ground Control Points (GCPs) as referred to in stereo literature. Our formulation explicitly models the influences of GCPs in a Markov Random Field. A novel regularization prior is naturally integrated into a global inference framework in a principled way using the Bayes rule. Our probabilistic framework allows GCPs to be obtained from various modalities and provides a natural way to integrate information from various sensors.
In addressing non-Lambertian reflectance, we introduce a new invariant for stereo correspondence which allows completely arbitrary scene reflectance (bidirectional reflectance distribution functions - BRDFs). This invariant can be used to formulate a rank constraint on stereo matching when the scene is observed by several lighting configurations in which only the lighting intensity varies
Recent Advances in Image Restoration with Applications to Real World Problems
In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included
Design of a Robotic Inspection Platform for Structural Health Monitoring
Actively monitoring infrastructure is key to detecting and correcting problems before they become costly. The vast scale of modern infrastructure poses a challenge to monitoring due to insufficient personnel. Certain structures, such as refineries, pose additional challenges and can be expensive, time-consuming, and hazardous to inspect.
This thesis outlines the development of an autonomous robot for structural-health-monitoring. The robot is capable of operating autonomously in level indoor environments and can be controlled manually to traverse difficult terrain. Both visual and lidar SLAM, along with a procedural-mapping technique, allow the robot to capture colored-point-clouds.
The robot is successfully able to automate the point cloud collection of straightforward environments such as hallways and empty rooms. While it performs well in these situations, its accuracy suffers in complex environments with variable lighting. More work is needed to create a robust system, but the potential time savings and upgrades make the concept promising
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