@inproceedings{parkison2023harmonic,
  title        = {The Harmonic Exponential Filter for Recursive Nonparametric Estimation on Motion Groups},
  author       = {Steven A. Parkison and Miguel Saavedra-Ruiz and Ria Arora and James Richard Forbes and Liam Paull},
  year         = {2023},
  booktitle    = {Robotic Perception and Mapping: Frontier Vision \& Learning Techniques @ IROS 2023},
  volume       = {},
  number       = {},
  pages        = {},
  doi          = {}
} 

A nonparametric and multimodal filtering approach that leverages harmonic exponential distributions.

Bayesian estimation is a vital tool in robotics as it allows systems to update
the belief of the robot state using incomplete information from noisy sensors. 
To render the state estimation problem tractable, many systems assume that the 
motion and measurement noise, as well as the state distribution, are all 
unimodal and Gaussian. However, there are numerous scenarios and systems that 
do not comply with these assumptions. Existing non-parametric filters that are 
used to model multimodal distributions have drawbacks that limit their ability 
to represent a diverse set of distributions. In this paper, we introduce a 
novel approach to nonparametric Bayesian filtering to cope with multimodal 
distributions using harmonic exponential distributions. This approach leverages 
two key insights of harmonic exponential distributions: a) the product of two 
distributions can be expressed as the element-wise addition of their log-likelihood 
Fourier coefficients, and b) the convolution of two distributions can be efficiently 
computed as the tensor product of their Fourier coefficients. These observations 
enable the development of an efficient and exact solution to the Bayes filter up 
to the band limit of a Fourier transform.  We demonstrate our filter's superior 
performance compared with established nonparametric filtering methods across a 
range of simulated and real-world localization tasks.
                      

@article{morin2023one,
	title        = {One-4-All: Neural Potential Fields for Embodied Navigation},
	author       = {Morin, Sacha and Saavedra-Ruiz, Miguel and Paull, Liam},
	year         = 2023,
	journal      = {arXiv preprint arXiv:2303.04011}
}

An end-to-end fully parametric method for image-goal navigation that leverages self-supervised and manifold learning to replace a topological graph with a geodesic regressor. During navigation, the geodesic regressor is used as an attractor in a potential function defined in latent space, allowing to frame navigation as a minimization problem.

A fundamental task in robotics is to navigate between two locations.
In particular, real-world navigation can require long-horizon planning
using high-dimensional RGB images, which poses a substantial challenge
for end-to-end learning-based approaches. Current semi-parametric methods
instead achieve long-horizon navigation by combining learned modules with
a topological memory of the environment, often represented as a graph over
previously collected images. However, using these graphs in practice typically
involves tuning a number of pruning heuristics to avoid spurious edges, limit
runtime memory usage and allow reasonably fast graph queries. In this work,
we present One-4-All (O4A), a method leveraging self-supervised and manifold
learning to obtain a graph-free, end-to-end navigation pipeline in which the
goal is specified as an image. Navigation is achieved by greedily minimizing
a potential function defined continuously over the O4A latent space. Our system
is trained offline on non-expert exploration sequences of RGB data and controls,
and does not require any depth or pose measurements. We show that O4A can reach
long-range goals in 8 simulated Gibson indoor environments, and further demonstrate
successful real-world navigation using a Jackal UGV platform.
                      

@article{saavedra2022monocular,
	title        = {Monocular Robot Navigation with Self-Supervised Pretrained Vision Transformers},
	author       = {Saavedra-Ruiz, Miguel and Morin, Sacha and Paull, Liam},
	year         = 2022,
	journal      = {arXiv preprint arXiv:2203.03682}
}                     

Visual Servoing navigation using pre-trained Self-Supervised Vision Transformers.

In this work, we consider the problem of learning a perception model for
monocular robot navigation using few annotated images. Using a Vision
Transformer (ViT) pretrained with a label-free self-supervised method, we
successfully train a coarse image segmentation model for the Duckietown
environment using 70 training images. Our model performs coarse image
segmentation at the 8x8  patch level, and the inference resolution can be
adjusted to balance prediction granularity and real-time perception constraints.
We study how best to adapt a ViT to our task and environment, and find that some
lightweight architectures can yield good single-image segmentations at a usable
frame rate, even on CPU. The resulting perception model is used as the backbone
for a simple yet robust visual servoing agent, which we deploy on a differential
drive mobile robot to perform two tasks: lane following and obstacle avoidance. 

@article{9656574,
	title        = {Monocular Visual Autonomous Landing System for Quadcopter Drones Using Software in the Loop},
	author       = {Saavedra-Ruiz, Miguel and Pinto-Vargas, Ana Maria and Romero-Cano, Victor},
	year         = 2022,
	journal      = {IEEE Aerospace and Electronic Systems Magazine},
	volume       = 37,
	number       = 5,
	pages        = {2--16},
	doi          = {10.1109/MAES.2021.3115208}
}  

My BEng degree project addressed the problem of the autonomous landing of a UAV
with a landing platform located on the top of a ground vehicle. The project
utilized vision-based techniques to detect the landing platform, a Kalman filter
was tailored for the tracking phase and finally, a PID controller sent control
commands to the flight controller of the UAV to land properly on the platform.
Rigorous assessments were conducted through the simulation of the whole robotic
stack with ROS and gazebo in the software in the loop provided by PX4.
Ultimately, the system was tested in a custom DJI F-450 and embedded in a Odroid
XU4. The system demonstrates a satisfactory performance and was able to land
with a mean error of ten centimeters from the center of the landing platform
(Implemented in Python, C++/Linux).

Autonomous landing is a capability that is essential to achieve the full
potential of multi-rotor drones in many social and industrial applications. The
implementation and testing of this capability on physical platforms is risky and
resource-intensive; hence, in order to ensure both a sound design process and a
safe deployment, simulations are required before implementing a physical
prototype. This paper presents the development of a monocular visual system,
using a software-in-the-loop methodology, that autonomously and efficiently
lands a quadcopter drone on a predefined landing pad, thus reducing the risks of
the physical testing stage. In addition to ensuring that the autonomous landing
system as a whole fulfils the design requirements using a Gazebo-based
simulation, our approach provides a tool for safe parameter tuning and design
testing prior to physical implementation. Finally, the proposed monocular
vision-only approach to landing pad tracking made it possible to effectively
implement the system in an F450 quadcopter drone with the standard computational
capabilities of an Odroid XU4 embedded processor.  

@inproceedings{salazargomez2021highlevel,
      title={High-level camera-LiDAR fusion for 3D object detection with machine learning},
      author={Gustavo A. Salazar-Gomez and Miguel A. Saavedra-Ruiz and Victor A. Romero-Cano},
      booktitle={Proceedings of the LatinX Workshop at CVPR},
      year={2021},
      publisher={CVPR},
}  

3D object detection is a problem that has gained popularity among the research
community due to its extensiveset of application on autonomous navigation,
surveillance and pick-and-place. Most of the solutions proposed in the
state-of-the-art are based on deep learning techniques and present astonishing
results in terms of accuracy. Nevertheless, a set of problems inherits from this
sort of solutions such as the need of enormous tagged datasets, extensive
computational resources due to the complexity of the model and most of the time,
no real-time inference. This work proposes an end-to-end classic Machine
Learning (ML) pipeline to solve the 3D object detection problem for cars. The
proposed method is leveraged on the use of frustum region proposals to segment
and estimate the parameters of the amodal 3D bounding box. Here we do not deal
with the problem of 2D object detection as for most of the research community
this is considered solved with ConvolutionalNeural Networks (CNN).

This task is addressed employing different ML techniques such as RANSAC for road
segmentation and DBSCAN for clustering. Global features are extracted out of the
segmented point cloud using The Ensemble of Shape Functions (ESF). Some feature
are engineered through PCA and statistics. Finally, the amodal 3D bounding box
parameters are estimated through a SVR regressor. 

@inproceedings{8588112,
	title        = {Detection and tracking of a landing platform for aerial robotics applications},
	author       = {M. S. {Ruiz} and A. M. P. {Vargas} and V. R. {Cano}},
	year         = 2018,
	booktitle    = {2018 IEEE 2nd Colombian Conference on Robotics and Automation (CCRA)},
	volume       = {},
	number       = {},
	pages        = {1--6}
}                     

Aerial robotic applications need to be endowed with systems capable to
accurately locate objects of interest to perform specific tasks at hand. I
Developed an embedded vision-based landing platform detection and tracking
system with ROS and OpenCV. The system extended the capabilities of a SURF-based
feature detector-descriptor that makes detections of a landing pad alongside a
Kalman filter-based estimation module. The system demonstrated a considerable
improvement over only-detector methods, diminishing the detection error and
providing accurate estimations of the landing pad position (Implemented in
C++/Linux). 

Style-transfer implementarion based on the paper A neural algorithm of artistic style using VGG-19.

Implementation of Style-Transfer based on the original paper 'A neural algorithm
of artistic style'. As stated by the authors, style-transfer is 'an artificial
system based on a Deep Neural Network that creates artistic images of high
perceptual quality'. In this work we aimed to replicate the original paper in a
Pytorch-based implementation using as backbone a VGG-19 model. The results
present how the implementation is capable to transfer the desired content and
style to a different image and even create an aesthetic result from scratch.

This work was locally deployed using docker-compose and web sockets in order the
create a seamlessly implementation of style-transfer with an intuitive GUI. 

Robot localization, Mapping, SLAM, path planning and navigation.

 Implementation of different robotics vision projects. These are based on the
robotics software engineer nanodegree provided by Udacity. All the projects were
developed with ROS and tested in a gazebo-based simulated environment. Different
topics were addressed such as Gazebo basic, ROS, robot localization, Mapping,
SLAM, navigation and path planning. The projects are listed below

    * How to use the model and building editor, plugins in Gazebo and more.
    * Creating a ball chaser robot in ROS and Gazebo.
    * Localization through Kalman Filter, Monte Carlo methods and ACML.
    * Occupancy grid map generation, GridBased Fast Slam ROS package and
      RTAB-Map SLAM.
    * The A* algorithm and robot navigation.

Stereo Visual Odometry and Visual Inertial Odometry pipelines to estimate the pose of a quad-rotor.

Implemented Stereo Visual Odometry and Visual Inertial Odometry pipelines to
estimate the pose of a quad-rotor. The system works by taking subsequent image
pairs and matching features throughout the test. Once those features are
obtained, 3d-points coordinates were retrieved with the depth map of the images
and the extrinsic camera calibration  matrix. Finally, the trajectory is
estimated using 3D-2D Perspective-n-Point (PNP). As an additional step, the VO
trajectory was used with the IMU data in an Error-State Extended Kalman Filter
to estimate the pose even when most of the VO observations were dropped. Both VO
and VIO showed good results estimating the trajectory of the UAV (Implemented in
Python/Linux). 

Simulation of an autonomous landing system for a UAV with Gazebo, ROS and the Software in the loop provided by PX4.

Simulated an autonomous landing system for a UAV with Gazebo, ROS and the
Software in the loop provided by PX4. The project consisted in the development
of different packages in C++ and Python which allowed the assessment of an
autonomous landing system. This robotics simulation allowed the thoroughly
development and evaluation of a landing pipeline for a UAV (Implemented in
Python, C++/ Linux). 

Lunar Lander, Mountain Car and Pendulum classical control tasks solved using RL.

Trained a lunar lander in a simulated environment with Reinforcement Learning.
The agent was implemented with the Expected-Sarsa algorithm and used a Neural
networkfor for action-values approximation. The algorithm was capable to do
planning steps with experience replay and learn a policy for the landing of the
agent. Thorughout the specialization I implemented different projects, some of
those are listed below.

    * Solved a Gridworld city with Dynamic programming to find an optimal
      policie.
    * Implemented a Dyna-Q and Dyna-Q+ algorithms in a changing maze environment
      to assess the performance of planning methods in RL.
    * Implemented an Average Reward Softmax Actor-Critic algorithm using
      Tile-coding to solve the Pendulum Swing-Up continuous problem.
    * Solved the Mountain car and Lunar Lander problems. 

Teleoperation system for a car-like robot through mathematical modelling.

Mathematical modelling of unmanned groundvehicles (UGV) is a well studied
problem in robotics and essential for the control of a robot. Developed a
teleoperation system for a car-like robot. The system received velocities in the
local coordinate frame of the robot and through the inverse kinematics model of
the vehicle these velocity commands were transformed to wheels’ speed and sent
to the vehicle’s motors. This project was embedded in a Raspberry Pi3 to allow
the remote control of the vehicle with a host computer through WIFI (Implemented
in C/Linux). 

Simulation of a localization and mapping system (laser-based SLAM) for a turtlebot2 in indoors.

Equipping robotic systems with novel localization and navigation stacks is
crucial for autonomous navigation. A localization and mapping system
(laser-based SLAM) for a turtlebot2 in indoors was simulated. The system was
capable to accurately localize the robot in a previously mapped environment and
subsequently navigate to a specific position in an occupancy grid map
(Implemented in C++/Linux). 

Wheeled-robot mathematical modelling through dynamical modelling (tire model), lateral and longitudinal control, state estimation with Kalman filters, visual perception and motion planning for self-driving vehicles.

This work involved four capstone projects in the area of self-driving cars.
Topics such as wheeled-robot mathematical modelling through dynamical modelling
(tire model), lateral and longitudinal control, state estimation with Kalman
filters, visual perception and motion planning were addressed. Most of the
projects were tested in the Carla Simulator to assess performance. A description
of the projects developed are presented below.

    * Control of a car-like robot through a longitudinal and lateral controller.
      The longitudinal controller was implemented with a PID and the lateral
      controller was a cross-track error controller.
    * Implementation of an error state extended Kalman Filter for the estimation
      of the trajectory of a vehicle. The filter fused information from a GNSS
      and IMU alongside the dynamic model of the vehicle to produce an accurate
      destimation of its trajectory on the space.
    * Robotics perception stack which detected the drivable space of the vehicle
      through image segmentation. Canny edge detector was used to detect the
      lines of the road and a depth representation of the scene was employed to
      estimate the distance-to-objects in the road and avoid collision using only
      image-based methods.
    * Implemented a navigation stack in the Carla simulator with the use of grid
      world representations  and state machines for a simple navigation strategy. 

Implemented a low-cost license plate recognition systems using deep learning techniques.

Automatic license plate recognition (LPR) is indispensable for the admission and
flow control of vehicles into parking lots orcondominiums.  Generally, these
systems are based on classic computer vision techniques owing to their
processing speed, however, these approaches can lead to inaccurate detections
and vague performance on non-ideal environmental conditions. My work tried to
surpass these setbacks with the implementation of an image-based plate
recognition system using convolutional neural networks (CNN) to enhance the
current methods. The system was optimized and embedded in a Nvidia Jetson Nano
to run in a low-cost computer at a recognition rate of 100ms per plate making it
ideal to operate in the places mentioned before (Implemented in Python, C++,
TensorRT/Linux). 

Heat map generator based on computer vision techniques to stochastically estimate the most visited areas in an indoor space with a monocular camera.

Implemented a heat map generator based on computer vision techniques to
stochastically estimate the most visited areas in an indoor space with a
monocular camera. A feature tracker method was used to estimate the average flow
of persons and a deep convolutional neural network was employed to obtain the
segmentation of the floor in the scene. This information was merge together to
gather relevant information about the people habits in shopping centers or
crowded areas (Implemented in Python/ Linux)