134 research outputs found
Kodizajn arhitekture i algoritama za lokalizacijumobilnih robota i detekciju prepreka baziranih namodelu
This thesis proposes SoPC (System on a Programmable Chip) architectures for efficient embedding of vison-based localization and obstacle detection tasks in a navigational pipeline on autonomous mobile robots. The obtained results are equivalent or better in comparison to state-ofthe- art. For localization, an efficient hardware architecture that supports EKF-SLAM's local map management with seven-dimensional landmarks in real time is developed. For obstacle detection a novel method of object recognition is proposed - detection by identification framework based on single detection window scale. This framework allows adequate algorithmic precision and execution speeds on embedded hardware platforms.Ova teza bavi se dizajnom SoPC (engl. System on a Programmable Chip) arhitektura i algoritama za efikasnu implementaciju zadataka lokalizacije i detekcije prepreka baziranih na viziji u kontekstu autonomne robotske navigacije. Za lokalizaciju, razvijena je efikasna računarska arhitektura za EKF-SLAM algoritam, koja podržava skladištenje i obradu sedmodimenzionalnih orijentira lokalne mape u realnom vremenu. Za detekciju prepreka je predložena nova metoda prepoznavanja objekata u slici putem prozora detekcije fiksne dimenzije, koja omogućava veću brzinu izvršavanja algoritma detekcije na namenskim računarskim platformama
A survey on real-time 3D scene reconstruction with SLAM methods in embedded systems
The 3D reconstruction of simultaneous localization and mapping (SLAM) is an
important topic in the field for transport systems such as drones, service
robots and mobile AR/VR devices. Compared to a point cloud representation, the
3D reconstruction based on meshes and voxels is particularly useful for
high-level functions, like obstacle avoidance or interaction with the physical
environment. This article reviews the implementation of a visual-based 3D scene
reconstruction pipeline on resource-constrained hardware platforms. Real-time
performances, memory management and low power consumption are critical for
embedded systems. A conventional SLAM pipeline from sensors to 3D
reconstruction is described, including the potential use of deep learning. The
implementation of advanced functions with limited resources is detailed. Recent
systems propose the embedded implementation of 3D reconstruction methods with
different granularities. The trade-off between required accuracy and resource
consumption for real-time localization and reconstruction is one of the open
research questions identified and discussed in this paper
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
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
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