47 research outputs found
Driving in Dense Traffic with Model-Free Reinforcement Learning
Traditional planning and control methods could fail to find a feasible
trajectory for an autonomous vehicle to execute amongst dense traffic on roads.
This is because the obstacle-free volume in spacetime is very small in these
scenarios for the vehicle to drive through. However, that does not mean the
task is infeasible since human drivers are known to be able to drive amongst
dense traffic by leveraging the cooperativeness of other drivers to open a gap.
The traditional methods fail to take into account the fact that the actions
taken by an agent affect the behaviour of other vehicles on the road. In this
work, we rely on the ability of deep reinforcement learning to implicitly model
such interactions and learn a continuous control policy over the action space
of an autonomous vehicle. The application we consider requires our agent to
negotiate and open a gap in the road in order to successfully merge or change
lanes. Our policy learns to repeatedly probe into the target road lane while
trying to find a safe spot to move in to. We compare against two
model-predictive control-based algorithms and show that our policy outperforms
them in simulation.Comment: Proceedings of the IEEE International Conference on Robotics and
Automation (ICRA), 2020. Updated Github repository link
Towards High-Performance Solid-State-LiDAR-Inertial Odometry and Mapping
We present a novel tightly-coupled LiDAR-inertial odometry and mapping scheme
for both solid-state and mechanical LiDARs. As frontend, a feature-based
lightweight LiDAR odometry provides fast motion estimates for adaptive keyframe
selection. As backend, a hierarchical keyframe-based sliding window
optimization is performed through marginalization for directly fusing IMU and
LiDAR measurements. For the Livox Horizon, a newly released solid-state LiDAR,
a novel feature extraction method is proposed to handle its irregular scan
pattern during preprocessing. LiLi-OM (Livox LiDAR-inertial odometry and
mapping) is real-time capable and achieves superior accuracy over
state-of-the-art systems for both LiDAR types on public data sets of mechanical
LiDARs and in experiments using the Livox Horizon. Source code and recorded
experimental data sets are available on Github.Comment: 15 page
Invariant Smoothing for Localization: Including the IMU Biases
In this article we investigate smoothing (i.e., optimisation-based)
estimation techniques for robot localization using an IMU aided by other
localization sensors. We more particularly focus on Invariant Smoothing (IS), a
variant based on the use of nontrivial Lie groups from robotics. We study the
recently introduced Two Frames Group (TFG), and prove it can fit into the
framework of Invariant Smoothing in order to better take into account the IMU
biases, as compared to the state-of-the-art in robotics. Experiments based on
the KITTI dataset show the proposed framework compares favorably to the
state-of-the-art smoothing methods in terms of robustness in some challenging
situations
Computational classification of animals for a highway detection system
As colisões entre veÃculos e animais representam um sério problema na infraestrutura rodoviária. Para evitar tais acidentes, medidas mitigatórias têm sido aplicadas em diferentes regiões do mundo. Neste projeto é apresentado um sistema de detecção de animais em rodovias utilizando visão computacional e algoritmo de aprendizado de máquina. Os modelos foram treinados para classificar dois grupos de animais: capivaras e equÃdeos. Foram utilizadas duas variantes da rede neural convolucional chamada Yolo (você só vê uma vez) — Yolov4 e Yolov4-tiny (versão mais leve da rede) — e o treinamento foi realizado a partir de modelos pré-treinados. Testes de detecção foram realizados em 147 imagens e os resultados de precisão obtidos foram de 84,87% e 79,87% para Yolov4 e Yolov4-tiny, respectivamente. O sistema proposto tem o potencial de melhorar a segurança rodoviária reduzindo ou prevenindo acidentes com animais.Vehicle-animal collisions represent a serious problem in roadway infrastructure. To avoid these roadway collisions, different mitigation systems have been applied in various regions of the world. In this article, a system for detecting animals on highways is presented using computer vision and machine learning algorithms. The models were trained to classify two groups of animals: capybaras and donkeys. Two variants of the convolutional neural network called Yolo (You only look once) were used, Yolov4 and Yolov4-tiny (a lighter version of the network). The training was carried out using pre-trained models. Detection tests were performed on 147 images. The accuracy results obtained were 84.87% and 79.87% for Yolov4 and Yolov4-tiny, respectively. The proposed system has the potential to improve road safety by reducing or preventing accidents with animals