26 research outputs found
A review of point set registration: from pairwise registration to groupwise registration
Abstract: This paper presents a comprehensive literature review on point set registration. The state-of-the-art modeling methods and algorithms for point set registration are discussed and summarized. Special attention is paid to methods for pairwise registration and groupwise registration. Some of the most prominent representative methods are selected to conduct qualitative and quantitative experiments. From the experiments we have conducted on 2D and 3D data, CPD-GL pairwise registration algorithm [1] and JRMPC groupwise registration algorithm [2,3] seem to outperform their rivals both in accuracy and computational complexity. Furthermore, future research directions and avenues in the area are identified
Robust mobile robot localization based on a security laser: An industry case study
This paper aims to address a mobile robot localization system that avoids using a dedicated laser scanner, making it possible to reduce implementation costs and the robot's size. The system has enough precision and robustness to meet the requirements of industrial environments. Design/methodology/approach - Using an algorithm for artificial beacon detection combined with a Kalman Filter and an outlier rejection method, it was possible to enhance the precision and robustness of the overall localization system. Findings - Usually, industrial automatic guide vehicles feature two kinds of lasers: one for navigation placed on top of the robot and another for obstacle detection (security lasers). Recently, security lasers extended their output data with obstacle distance (contours) and reflectivity. These new features made it possible to develop a novel localization system based on a security laser. Research limitations/implications - Once the proposed methodology is completely validated, in the future, a scheme for global localization and failure detection should be addressed. Practical implications - This paper presents a comparison between the presented approach and a commercial localization system for industry. The proposed algorithms were tested in an industrial application under realistic working conditions. Social implications - The presented methodology represents a gain in the effective cost of the mobile robot platform, as it discards the need for a dedicated laser for localization purposes. Originality/value - This paper presents a novel approach that benefits from the presence of a security laser on mobile robots (mandatory sensor when considering industrial applications), using it simultaneously with other sensors, not only to guarantee safety conditions during operation but also to locate the robot in the environment. This paper is also valuable because of the comparison made with a commercialized system, as well as the tests conducted in real industrial environments, which prove that the approach presented is suitable for working under these demanding conditions.Project "TEC4Growth" - Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020" is fnanced by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF).info:eu-repo/semantics/publishedVersio
Computationally-efficient visual inertial odometry for autonomous vehicle
This thesis presents the design, implementation, and validation of a novel nonlinearfiltering
based Visual Inertial Odometry (VIO) framework for robotic navigation in GPSdenied
environments. The system attempts to track the vehicle’s ego-motion at each time
instant while capturing the benefits of both the camera information and the Inertial Measurement
Unit (IMU). VIO demands considerable computational resources and processing
time, and this makes the hardware implementation quite challenging for micro- and nanorobotic
systems. In many cases, the VIO process selects a small subset of tracked features
to reduce the computational cost. VIO estimation also suffers from the inevitable accumulation
of error. This limitation makes the estimation gradually diverge and even fail to
track the vehicle trajectory over long-term operation. Deploying optimization for the entire
trajectory helps to minimize the accumulative errors, but increases the computational cost
significantly. The VIO hardware implementation can utilize a more powerful processor
and specialized hardware computing platforms, such as Field Programmable Gate Arrays,
Graphics Processing Units and Application-Specific Integrated Circuits, to accelerate the
execution. However, the computation still needs to perform identical computational steps
with similar complexity. Processing data at a higher frequency increases energy consumption
significantly. The development of advanced hardware systems is also expensive and
time-consuming. Consequently, the approach of developing an efficient algorithm will be
beneficial with or without hardware acceleration. The research described in this thesis
proposes multiple solutions to accelerate the visual inertial odometry computation while
maintaining a comparative estimation accuracy over long-term operation among state-ofthe-
art algorithms.
This research has resulted in three significant contributions. First, this research involved
the design and validation of a novel nonlinear filtering sensor-fusion algorithm using trifocal
tensor geometry and a cubature Kalman filter. The combination has handled the system
nonlinearity effectively, while reducing the computational cost and system complexity significantly.
Second, this research develops two solutions to address the error accumulation
issue. For standalone self-localization projects, the first solution applies a local optimization
procedure for the measurement update, which performs multiple corrections on a single
measurement to optimize the latest filter state and covariance. For larger navigation
projects, the second solution integrates VIO with additional pseudo-ranging measurements
between the vehicle and multiple beacons in order to bound the accumulative errors. Third,
this research develops a novel parallel-processing VIO algorithm to speed up the execution
using a multi-core CPU. This allows the distribution of the filtering computation on each
core to process and optimize each feature measurement update independently.
The performance of the proposed visual inertial odometry framework is evaluated using
publicly-available self-localization datasets, for comparison with some other open-source
algorithms. The results illustrate that a proposed VIO framework is able to improve the
VIO’s computational efficiency without the installation of specialized hardware computing
platforms and advanced software libraries
Point Cloud Registration Based on Direct Deep Features With Applications in Intelligent Vehicles
Point cloud registration is widely used in the research of intelligent vehicles, typical problems include map matching, visual odometer, pose estimation, etc. This paper proposes a deep learning-based registration method that can input point clouds directly, thereby preventing information loss of preprocessing needed by alternative deep-learning approaches. Our network, named DPFNet (Direct Point Feature Net), gradually downsamples the point cloud and aggregates points around determined reference points to formulate local features automatically. This is facilitated by a novel convolution-like operator and a novel loss function. The points in the point cloud are mapped to a high dimensional embedding through the designed deep neural network, where every embedding reflects the local feature of a specific spatial area. Based on the embedding features, correspondences between points can be estimated robustly and the registration between the point clouds can be obtained using an external geometric optimization algorithm. Experimental results on open benchmarks validate the proposed method and show that its performance is favourable over several baseline methods. Specifically, we test the proposed algorithm on KITTI benchmark, which shows its potential in tasks of intelligent vehicles, e.g., map matching, visual or LiDAR odometer
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Comparison of state marginalization techniques in visual inertial navigation filters
The main focus of this thesis is finding and validating an efficient visual inertial
navigation system (VINS) algorithm for applications in micro aerial vehicles (MAV).
A typical VINS for a MAV consists of a low-cost micro electro mechanical system
(MEMS) inertial measurement unit (IMU) and a monocular camera, which provides a
minimum payload sensor setup. This setup is highly desirable for navigation of MAVs
because highly resource constrains in the platform. However, bias and noise of lowcost
IMUs demand sufficiently accurate VINS algorithms. Accurate VINS algorithms
has been developed over the past decade but they demand higher computational
resources. Therefore, resource limited MAVs demand computationally efficient VINS
algorithms.
This thesis considers the following computational cost elements in the VINS algorithm:
feature tracking front-end, state marginalization technique and the complexity
of the algorithm formulation. In this thesis three state-of-the-art feature
tracking front ends were compared in terms of accuracy. (VINS-Mono front-end,
MSCKF-Mono feature tracker and Matlab based feature tracker). Four state-ofthe-
art state marginalization techniques (MSCKF-Generic marginalization, MSCKFMono
marginalization, MSCKF-Two way marginalization and Two keyframe based
epipolar constraint marginalization) were compared in terms of accuracy and efficiency.
The complexity of the VINS algorithm formulation has also been compared
using the filter execution time.
The research study then presents the comparative analysis of the algorithms using a
publicly available MAV benchmark datasets. Based on the results, an efficient VINS
algorithm is proposed which is suitable for MAVs
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Leader-assisted localization approach for a heterogeneous multi-robot system
This thesis presents the design, implementation, and validation of a novel leader assisted localization framework for a heterogeneous multi-robot system (MRS) with sensing and communication range constraints. It is assumed that the given heterogeneous MRS has a more powerful robot (or group of robots) with accurate self localization capabilities (leader robots) while the rest of the team (child robots), i.e. less powerful robots, is localized with the assistance of leader robots and inter-robot observation between teammates. This will eventually pose a condition that the child
robots should be operated within the sensing and communication range of leader
robots. The bounded navigation space therefore may require added algorithms to
avoid inter-robot collisions and limit robots’ maneuverability. To address this limitation,
first, the thesis introduces a novel distributed graph search and global pose composition
algorithm to virtually enhance the leader robots’ sensing and communication
range while avoiding possible double counting of common information. This allows
child robots to navigate beyond the sensing and communication range of the leader
robot, yet receive localization services from the leader robots. A time-delayed measurement
update algorithm and a memory optimization approach are then integrated
into the proposed localization framework. This eventually improves the robustness
of the algorithm against the unknown processing and communication time-delays associated
with the inter-robot data exchange network. Finally, a novel hierarchical sensor fusion architecture is introduced so that the proposed localization scheme can
be implemented using inter-robot relative range and bearing measurements.
The performance of the proposed localization framework is evaluated through a series
of indoor experiments, a publicly available multi-robot localization and mapping
data-set and a set of numerical simulations. The results illustrate that the proposed
leader-assisted localization framework is capable of establishing accurate and nonoverconfident
localization for the child robots even when the child robots operate
beyond the sensing and communication boundaries of the leader robots
Predictive parameter estimation for Bayesian filtering
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 113-117).In this thesis, I develop CELLO, an algorithm for predicting the covariances of any Gaussian model used to account for uncertainty in a complex system. The primary motivation for this work is state estimation; often, complex raw sensor measurements are processed into low dimensional observations of a vehicle state. I argue that the covariance of these observations can be well-modelled as a function of the raw sensor measurement, and provide a method to learn this function from data. This method is computationally cheap, asymptotically correct, easy to extend to new sensors, and noninvasive, in the sense that it augments, rather than disrupts, existing filtering algorithms. I additionally present two important variants; first, I extend CELLO to learn even when ground truth vehicle states are unavailable; and second, I present an equivalent Bayesian algorithm. I then use CELLO to learn covariance models for several systems, including a laser scan-matcher, an optical flow system, and a visual odometry system. I show that filtering using covariances predicted by CELLO can quantitatively improve estimator accuracy and consistency, both relative to a fixed covariance model and relative to carefully tuned domain-specific covariance models.by William Vega-Brown.S.M
Directional Estimation for Robotic Beating Heart Surgery
In robotic beating heart surgery, a remote-controlled robot can be used to carry out the operation while automatically canceling out the heart motion. The surgeon controlling the robot is shown a stabilized view of the heart. First, we consider the use of directional statistics for estimation of the phase of the heartbeat. Second, we deal with reconstruction of a moving and deformable surface. Third, we address the question of obtaining a stabilized image of the heart