29 research outputs found
Development of a probabilistic perception system for camera-lidar sensor fusion
La estimaci贸n de profundidad usando diferentes sensores es uno de los desaf铆os clave para dotar a las m谩quinas aut贸nomas de s贸lidas capacidades de percepci贸n rob贸tica. Ha habido un avance sobresaliente en el desarrollo de t茅cnicas de estimaci贸n de profundidad unimodales basadas en c谩maras monoculares, debido a su alta resoluci贸n o sensores LiDAR, debido a los datos geom茅tricos precisos que proporcionan. Sin embargo, cada uno de ellos presenta inconvenientes inherentes, como la alta sensibilidad a los cambios en las condiciones de iluminaci贸n en el caso delas c谩maras y la resoluci贸n limitada de los sensores LiDAR. La fusi贸n de sensores se puede utilizar para combinar los m茅ritos y compensar las desventajas de estos dos tipos de sensores. Sin embargo, los m茅todos de fusi贸n actuales funcionan a un alto nivel. Procesan los flujos de datos de los sensores de forma independiente y combinan las estimaciones de alto nivel obtenidas para cada sensor. En este proyecto, abordamos el problema en un nivel bajo, fusionando los flujos de sensores sin procesar, obteniendo as铆 estimaciones de profundidad que son densas y precisas, y pueden usarse como una fuente de datos multimodal unificada para problemas de estimaci贸n de nivel superior. Este trabajo propone un modelo de campo aleatorio condicional (CRF) con m煤ltiples potenciales de geometr铆a y apariencia que representa a la perfecci贸n el problema de estimar mapas de profundidad densos a partir de datos de c谩mara y LiDAR. El modelo se puede optimizar de manera eficiente utilizando el algoritmo Conj煤gate Gradient Squared (CGS). El m茅todo propuesto se eval煤a y compara utilizando el conjunto de datos proporcionado por KITTI Datset. Adicionalmente, se eval煤a cualitativamente el modelo, usando datos adquiridos por el autor de est茅 trabajoMulti-modal depth estimation is one of the key challenges for endowing autonomous
machines with robust robotic perception capabilities. There has been an outstanding
advance in the development of uni-modal depth estimation techniques based
on either monocular cameras, because of their rich resolution or LiDAR sensors due
to the precise geometric data they provide. However, each of them suffers from some
inherent drawbacks like high sensitivity to changes in illumination conditions in
the case of cameras and limited resolution for the LiDARs. Sensor fusion can be
used to combine the merits and compensate the downsides of these two kinds of
sensors. Nevertheless, current fusion methods work at a high level. They processes
sensor data streams independently and combine the high level estimates obtained
for each sensor. In this thesis, I tackle the problem at a low level, fusing the raw
sensor streams, thus obtaining depth estimates which are both dense and precise,
and can be used as a unified multi-modal data source for higher level estimation
problems.
This work proposes a Conditional Random Field (CRF) model with multiple geometry
and appearance potentials that seamlessly represents the problem of estimating
dense depth maps from camera and LiDAR data. The model can be optimized
efficiently using the Conjugate Gradient Squared (CGS) algorithm. The proposed
method was evaluated and compared with the state-of-the-art using the commonly
used KITTI benchmark dataset. In addition, the model is qualitatively evaluated using
data acquired by the author of this work.Maestr铆aMag铆ster en Ingenier铆a de Desarrollo de Producto
Development of a probabilistic perception system for camera-lidar sensor fusion
La estimaci贸n de profundidad usando diferentes sensores es uno de los desaf铆os clave para dotar a las m谩quinas aut贸nomas de s贸lidas capacidades de percepci贸n rob贸tica. Ha habido un avance sobresaliente en el desarrollo de t茅cnicas de estimaci贸n de profundidad unimodales basadas en c谩maras monoculares, debido a su alta resoluci贸n o sensores LiDAR, debido a los datos geom茅tricos precisos que proporcionan. Sin embargo, cada uno de ellos presenta inconvenientes inherentes, como la alta sensibilidad a los cambios en las condiciones de iluminaci贸n en el caso delas c谩maras y la resoluci贸n limitada de los sensores LiDAR. La fusi贸n de sensores se puede utilizar para combinar los m茅ritos y compensar las desventajas de estos dos tipos de sensores. Sin embargo, los m茅todos de fusi贸n actuales funcionan a un alto nivel. Procesan los flujos de datos de los sensores de forma independiente y combinan las estimaciones de alto nivel obtenidas para cada sensor. En este proyecto, abordamos el problema en un nivel bajo, fusionando los flujos de sensores sin procesar, obteniendo as铆 estimaciones de profundidad que son densas y precisas, y pueden usarse como una fuente de datos multimodal unificada para problemas de estimaci贸n de nivel superior. Este trabajo propone un modelo de campo aleatorio condicional (CRF) con m煤ltiples potenciales de geometr铆a y apariencia que representa a la perfecci贸n el problema de estimar mapas de profundidad densos a partir de datos de c谩mara y LiDAR. El modelo se puede optimizar de manera eficiente utilizando el algoritmo Conj煤gate Gradient Squared (CGS). El m茅todo propuesto se eval煤a y compara utilizando el conjunto de datos proporcionado por KITTI Datset. Adicionalmente, se eval煤a cualitativamente el modelo, usando datos adquiridos por el autor de est茅 trabajoMulti-modal depth estimation is one of the key challenges for endowing autonomous
machines with robust robotic perception capabilities. There has been an outstanding
advance in the development of uni-modal depth estimation techniques based
on either monocular cameras, because of their rich resolution or LiDAR sensors due
to the precise geometric data they provide. However, each of them suffers from some
inherent drawbacks like high sensitivity to changes in illumination conditions in
the case of cameras and limited resolution for the LiDARs. Sensor fusion can be
used to combine the merits and compensate the downsides of these two kinds of
sensors. Nevertheless, current fusion methods work at a high level. They processes
sensor data streams independently and combine the high level estimates obtained
for each sensor. In this thesis, I tackle the problem at a low level, fusing the raw
sensor streams, thus obtaining depth estimates which are both dense and precise,
and can be used as a unified multi-modal data source for higher level estimation
problems.
This work proposes a Conditional Random Field (CRF) model with multiple geometry
and appearance potentials that seamlessly represents the problem of estimating
dense depth maps from camera and LiDAR data. The model can be optimized
efficiently using the Conjugate Gradient Squared (CGS) algorithm. The proposed
method was evaluated and compared with the state-of-the-art using the commonly
used KITTI benchmark dataset. In addition, the model is qualitatively evaluated using
data acquired by the author of this work.Maestr铆aMag铆ster en Ingenier铆a de Desarrollo de Producto
Understanding the Relational Dynamics of Chilean Rural Teachers: Contributions from a Narrative-Generative Perspective
The rural teaching profession demands a genuine commitment to the development of future
generations, ensuring a legacy that transcends time. Thus, generativity would be a characteristic
dimension of the teaching function manifested in various forms, roles and tasks aimed at caring
for students and their communities of origin. Objective: To explore the relational dynamics that
rural teachers have constructed throughout their life trajectories and how these have influenced the
potentially generative development of their teaching identity. Method: an interpretative-qualitative
approach was adopted, following a descriptive, exploratory and cross-sectional design. The purposive
sample consisted of twelve teachers with an average of 33 years of experience in rural schools in
the Metropolitan Region, La Araucan铆a and Los R铆os (Chile). In-depth interviews from a narrativegenerative
perspective were used to collect the data. Subsequently, the stories were subjected to
content analysis, following the logic of Grounded Theory. Results: The teachers show a potentially
generative development, expressed in the construction of relational dynamics of trust, reciprocity
and positive affection with their students. With their peers, they demonstrate collaborative practices,
teamwork and orientation towards continuous improvement in their professional work. At the
management level, they stand out for their leadership skills and commitment to the development of
rural communities.National Research and Development Agency/National Fund for Scientific and Technological Research of Chile (FONDECYT) 1119002
An integrated OPF dispatching model with wind power and demand response for day-ahead markets
In the day-ahead dispatching of network-constrained electricity markets, renewable energy and distributed resources are dispatched together with conventional generation. The uncertainty and volatility associated to renewable resources represents a new paradigm to be faced for power system operation. Moreover, in various electricity markets there are mechanisms to allow the demand participation through demand response (DR) strategies. Under operational and economic restrictions, the operator each day, or even in intra-day markets, dispatchs an optimal power flow to find a feasible state of operation. The operation decisions in power markets use an optimal power flow considering unit commitment to dispatch economically generation and DR resources under security restrictions. This paper constructs a model to include demand response in the optimal power flow under wind power uncertainty. The model is formulated as a mixed-integer linear quadratic problem and evaluated through Monte-Carlo simulations. A large number of scenarios around a trajectory bid captures the uncertainty in wind power forecasting. The proposed integrated OPF model is tested on the standard IEEE 39-bus system
Small batch deep reinforcement learning
In value-based deep reinforcement learning with replay memories, the batch
size parameter specifies how many transitions to sample for each gradient
update. Although critical to the learning process, this value is typically not
adjusted when proposing new algorithms. In this work we present a broad
empirical study that suggests {\em reducing} the batch size can result in a
number of significant performance gains; this is surprising, as the general
tendency when training neural networks is towards larger batch sizes for
improved performance. We complement our experimental findings with a set of
empirical analyses towards better understanding this phenomenon.Comment: Published at NeurIPS 202
Quantification of operating reserves with high penetration of wind power considering extreme values
The high integration of wind energy in power systems requires operating reserves to ensure the reliability and security in the operation. The intermittency and volatility in wind power sets a challenge for day-ahead dispatching in order to schedule generation resources. Therefore,the quantification of operating reserves is addressed in this paper using extreme values through Monte-Carlo simulations. The uncertainty inwind power forecasting is captured by a generalized extreme value distribution to generate scenarios. The day-ahead dispatching model is formulated asa mixed-integer linear quadratic problem including ramping constraints. This approach is tested in the IEEE-118 bus test system including integration of wind power in the system. The results represent the range of values for operating reserves in day-ahead dispatchin
Probabilistic Perception System for Object Classification Based on Camera -LiDAR Sensor Fusion
International audienceOne of the most basic needs to guide the definition of urban, agro-industrial and territorial management policies is to have a digital topographic representation or map of cities, crops and forests. These maps should ideally be created from multiple sensors whose responses are complementary (color information, for example, complements the returns of a LiDAR sensor in the presence of rain or low reflective objects). Once a topographic representation has been constructed, it can be used to produce and geo-localize higher-level estimates (e.g., location and classification of different trees and plants, crop density, location, and types of pests). Data can be collected using terrestrial unmanned vehicles equipped with hyper-spectral cameras, stereo cameras and LiDAR (Light Detection and Ranging) sensors. The processing of the acquired data can be used to generate a digital forest model (DFM). DFM will support forest planners in making multi-criteria decisions (MCDA) when planning harvesting operations. However creating a DFM or the map of a city, require a highly accurate and dense point cloud of the environment at hand. Motivated for building 3D reconstructions from which representations of different vegetation features of an environment can be obtained with high quality and precision. A robust perception system is proposed for densely predicting depth, since it is an essential component in understanding the 3D geometry of a scene. It is known that cameras provide near instantaneous capture of the workspace鈥檚 appearance such as texture and color, but from a single view, little geometrical information. On the other hand, laser readings may be so sparse that significant information about the surface is missing. The considerations above motivate the formulation of this work鈥檚 research question: How to develop a perception system for fusing a laser scan with a RGB image in order to produce a higher-resolution range
Bigger, Better, Faster: Human-level Atari with human-level efficiency
We introduce a value-based RL agent, which we call BBF, that achieves
super-human performance in the Atari 100K benchmark. BBF relies on scaling the
neural networks used for value estimation, as well as a number of other design
choices that enable this scaling in a sample-efficient manner. We conduct
extensive analyses of these design choices and provide insights for future
work. We end with a discussion about updating the goalposts for
sample-efficient RL research on the ALE. We make our code and data publicly
available at
https://github.com/google-research/google-research/tree/master/bigger_better_faster.Comment: ICML 2023 Camera Read