13 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
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
Probabilistic multi-modal depth estimation based on camera鈥揕iDAR sensor fusion
Multi-modal depth estimation is one of the key challenges for endowing autonomous machines with robust robotic perception capabilities. There have been outstanding advances 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 these suffers from some inherent drawbacks, such as 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 for the downsides of these two kinds of sensors. Nevertheless, current fusion methods work at a high level. They process the sensor data streams independently and combine the high-level estimates obtained for each sensor. In this paper, we 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 model with multiple geometry and appearance potentials. It 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 algorithm. The proposed method was evaluated and compared with the state of the art using the commonly used KITTI benchmark dataset
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
JaxPruner: A concise library for sparsity research
This paper introduces JaxPruner, an open-source JAX-based pruning and sparse
training library for machine learning research. JaxPruner aims to accelerate
research on sparse neural networks by providing concise implementations of
popular pruning and sparse training algorithms with minimal memory and latency
overhead. Algorithms implemented in JaxPruner use a common API and work
seamlessly with the popular optimization library Optax, which, in turn, enables
easy integration with existing JAX based libraries. We demonstrate this ease of
integration by providing examples in four different codebases: Scenic, t5x,
Dopamine and FedJAX and provide baseline experiments on popular benchmarks.Comment: Jaxpruner is hosted at http://github.com/google-research/jaxprune