27 research outputs found
PVI-DSO: Leveraging Planar Regularities for Direct Sparse Visual-Inertial Odometry
The monocular Visual-Inertial Odometry (VIO) based on the direct method can
leverage all the available pixels in the image to estimate the camera motion
and reconstruct the environment. The denser map reconstruction provides more
information about the environment, making it easier to extract structure and
planar regularities. In this paper, we propose a monocular direct sparse
visual-inertial odometry, which exploits the plane regularities (PVI-DSO). Our
system detects coplanar information from 3D meshes generated from 3D point
clouds and uses coplanar parameters to introduce coplanar constraints. In order
to reduce computation and improve compactness, the plane-distance cost is
directly used as the prior information of plane parameters. We conduct ablation
experiments on public datasets and compare our system with other
state-of-the-art algorithms. The experimental results verified leveraging the
plane information can improve the accuracy of the VIO system based on the
direct method
Incremental Visual-Inertial 3D Mesh Generation with Structural Regularities
Visual-Inertial Odometry (VIO) algorithms typically rely on a point cloud
representation of the scene that does not model the topology of the
environment. A 3D mesh instead offers a richer, yet lightweight, model.
Nevertheless, building a 3D mesh out of the sparse and noisy 3D landmarks
triangulated by a VIO algorithm often results in a mesh that does not fit the
real scene. In order to regularize the mesh, previous approaches decouple state
estimation from the 3D mesh regularization step, and either limit the 3D mesh
to the current frame or let the mesh grow indefinitely. We propose instead to
tightly couple mesh regularization and state estimation by detecting and
enforcing structural regularities in a novel factor-graph formulation. We also
propose to incrementally build the mesh by restricting its extent to the
time-horizon of the VIO optimization; the resulting 3D mesh covers a larger
portion of the scene than a per-frame approach while its memory usage and
computational complexity remain bounded. We show that our approach successfully
regularizes the mesh, while improving localization accuracy, when structural
regularities are present, and remains operational in scenes without
regularities.Comment: 7 pages, 5 figures, ICRA accepte
SPINS: Structure Priors aided Inertial Navigation System
Although Simultaneous Localization and Mapping (SLAM) has been an active
research topic for decades, current state-of-the-art methods still suffer from
instability or inaccuracy due to feature insufficiency or its inherent
estimation drift, in many civilian environments. To resolve these issues, we
propose a navigation system combing the SLAM and prior-map-based localization.
Specifically, we consider additional integration of line and plane features,
which are ubiquitous and more structurally salient in civilian environments,
into the SLAM to ensure feature sufficiency and localization robustness. More
importantly, we incorporate general prior map information into the SLAM to
restrain its drift and improve the accuracy. To avoid rigorous association
between prior information and local observations, we parameterize the prior
knowledge as low dimensional structural priors defined as relative
distances/angles between different geometric primitives. The localization is
formulated as a graph-based optimization problem that contains
sliding-window-based variables and factors, including IMU, heterogeneous
features, and structure priors. We also derive the analytical expressions of
Jacobians of different factors to avoid the automatic differentiation overhead.
To further alleviate the computation burden of incorporating structural prior
factors, a selection mechanism is adopted based on the so-called information
gain to incorporate only the most effective structure priors in the graph
optimization. Finally, the proposed framework is extensively tested on
synthetic data, public datasets, and, more importantly, on the real UAV flight
data obtained from a building inspection task. The results show that the
proposed scheme can effectively improve the accuracy and robustness of
localization for autonomous robots in civilian applications.Comment: 14 pages, 14 figure
Visual simultaneous localisation and mapping for sewer pipe networks leveraging cylindrical regularity
This work proposes a novel visual Simultaneous Localisation and Mapping (vSLAM) approach for robots in sewer pipe networks. One problem of vSLAM in pipes is that the scale drifts and accuracy degrades. We propose the use of structural information to mitigate this problem via cylindrical regularity. The main novelty consists of an approach for cylinder detection that is more robust than previous methods in non-smooth sewer pipe environments. Cylindrical regularity is then incorporated into both local bundle adjustment and pose graph optimisation. The approach adopts a minimal cylinder representation with only five parameters, avoiding constraints during the optimisation in vSLAM. A further novelty is that the estimated cylinder is part of the scale drift estimation, which enables a correction to the translation estimate and this further improves the accuracy. The approach, termed Cylindrical Regularity ORB-SLAM (CRORB), is benchmarked and compared to leading visual SLAM algorithms ORB-SLAM2 and direct sparse odometry (DSO), as well as a vSLAM algorithm with cylindrical regularity developed for gas pipes, using real sewer pipe data and synthetic data generated with the Gazebo modelling software. The results demonstrate that CRORB improves substantially over the competitors, with a reduction of approximately 70% in error on real data
PlaneSLAM: Plane-based LiDAR SLAM for Motion Planning in Structured 3D Environments
LiDAR sensors are a powerful tool for robot simultaneous localization and
mapping (SLAM) in unknown environments, but the raw point clouds they produce
are dense, computationally expensive to store, and unsuited for direct use by
downstream autonomy tasks, such as motion planning. For integration with motion
planning, it is desirable for SLAM pipelines to generate lightweight geometric
map representations. Such representations are also particularly well-suited for
man-made environments, which can often be viewed as a so-called "Manhattan
world" built on a Cartesian grid. In this work we present a 3D LiDAR SLAM
algorithm for Manhattan world environments which extracts planar features from
point clouds to achieve lightweight, real-time localization and mapping. Our
approach generates plane-based maps which occupy significantly less memory than
their point cloud equivalents, and are suited towards fast collision checking
for motion planning. By leveraging the Manhattan world assumption, we target
extraction of orthogonal planes to generate maps which are more structured and
organized than those of existing plane-based LiDAR SLAM approaches. We
demonstrate our approach in the high-fidelity AirSim simulator and in
real-world experiments with a ground rover equipped with a Velodyne LiDAR. For
both cases, we are able to generate high quality maps and trajectory estimates
at a rate matching the sensor rate of 10 Hz
RD-VIO: Robust Visual-Inertial Odometry for Mobile Augmented Reality in Dynamic Environments
It is typically challenging for visual or visual-inertial odometry systems to
handle the problems of dynamic scenes and pure rotation. In this work, we
design a novel visual-inertial odometry (VIO) system called RD-VIO to handle
both of these two problems. Firstly, we propose an IMU-PARSAC algorithm which
can robustly detect and match keypoints in a two-stage process. In the first
state, landmarks are matched with new keypoints using visual and IMU
measurements. We collect statistical information from the matching and then
guide the intra-keypoint matching in the second stage. Secondly, to handle the
problem of pure rotation, we detect the motion type and adapt the
deferred-triangulation technique during the data-association process. We make
the pure-rotational frames into the special subframes. When solving the
visual-inertial bundle adjustment, they provide additional constraints to the
pure-rotational motion. We evaluate the proposed VIO system on public datasets.
Experiments show the proposed RD-VIO has obvious advantages over other methods
in dynamic environments
Structure PLP-SLAM: Efficient Sparse Mapping and Localization using Point, Line and Plane for Monocular, RGB-D and Stereo Cameras
This paper demonstrates a visual SLAM system that utilizes point and line
cloud for robust camera localization, simultaneously, with an embedded
piece-wise planar reconstruction (PPR) module which in all provides a
structural map. To build a scale consistent map in parallel with tracking, such
as employing a single camera brings the challenge of reconstructing geometric
primitives with scale ambiguity, and further introduces the difficulty in graph
optimization of bundle adjustment (BA). We address these problems by proposing
several run-time optimizations on the reconstructed lines and planes. The
system is then extended with depth and stereo sensors based on the design of
the monocular framework. The results show that our proposed SLAM tightly
incorporates the semantic features to boost both frontend tracking as well as
backend optimization. We evaluate our system exhaustively on various datasets,
and open-source our code for the community
(https://github.com/PeterFWS/Structure-PLP-SLAM).Comment: The pre-print version, v2 add supplementary materials, code
open-source: https://github.com/PeterFWS/Structure-PLP-SLA