11,417 research outputs found
Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments
We present a self-supervised approach to ignoring "distractors" in camera
images for the purposes of robustly estimating vehicle motion in cluttered
urban environments. We leverage offline multi-session mapping approaches to
automatically generate a per-pixel ephemerality mask and depth map for each
input image, which we use to train a deep convolutional network. At run-time we
use the predicted ephemerality and depth as an input to a monocular visual
odometry (VO) pipeline, using either sparse features or dense photometric
matching. Our approach yields metric-scale VO using only a single camera and
can recover the correct egomotion even when 90% of the image is obscured by
dynamic, independently moving objects. We evaluate our robust VO methods on
more than 400km of driving from the Oxford RobotCar Dataset and demonstrate
reduced odometry drift and significantly improved egomotion estimation in the
presence of large moving vehicles in urban traffic.Comment: International Conference on Robotics and Automation (ICRA), 2018.
Video summary: http://youtu.be/ebIrBn_nc-
3D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle Detection
Cameras are a crucial exteroceptive sensor for self-driving cars as they are
low-cost and small, provide appearance information about the environment, and
work in various weather conditions. They can be used for multiple purposes such
as visual navigation and obstacle detection. We can use a surround multi-camera
system to cover the full 360-degree field-of-view around the car. In this way,
we avoid blind spots which can otherwise lead to accidents. To minimize the
number of cameras needed for surround perception, we utilize fisheye cameras.
Consequently, standard vision pipelines for 3D mapping, visual localization,
obstacle detection, etc. need to be adapted to take full advantage of the
availability of multiple cameras rather than treat each camera individually. In
addition, processing of fisheye images has to be supported. In this paper, we
describe the camera calibration and subsequent processing pipeline for
multi-fisheye-camera systems developed as part of the V-Charge project. This
project seeks to enable automated valet parking for self-driving cars. Our
pipeline is able to precisely calibrate multi-camera systems, build sparse 3D
maps for visual navigation, visually localize the car with respect to these
maps, generate accurate dense maps, as well as detect obstacles based on
real-time depth map extraction
Deep Drone Racing: From Simulation to Reality with Domain Randomization
Dynamically changing environments, unreliable state estimation, and operation
under severe resource constraints are fundamental challenges that limit the
deployment of small autonomous drones. We address these challenges in the
context of autonomous, vision-based drone racing in dynamic environments. A
racing drone must traverse a track with possibly moving gates at high speed. We
enable this functionality by combining the performance of a state-of-the-art
planning and control system with the perceptual awareness of a convolutional
neural network (CNN). The resulting modular system is both platform- and
domain-independent: it is trained in simulation and deployed on a physical
quadrotor without any fine-tuning. The abundance of simulated data, generated
via domain randomization, makes our system robust to changes of illumination
and gate appearance. To the best of our knowledge, our approach is the first to
demonstrate zero-shot sim-to-real transfer on the task of agile drone flight.
We extensively test the precision and robustness of our system, both in
simulation and on a physical platform, and show significant improvements over
the state of the art.Comment: Accepted as a Regular Paper to the IEEE Transactions on Robotics
Journal. arXiv admin note: substantial text overlap with arXiv:1806.0854
Robust Dense Mapping for Large-Scale Dynamic Environments
We present a stereo-based dense mapping algorithm for large-scale dynamic
urban environments. In contrast to other existing methods, we simultaneously
reconstruct the static background, the moving objects, and the potentially
moving but currently stationary objects separately, which is desirable for
high-level mobile robotic tasks such as path planning in crowded environments.
We use both instance-aware semantic segmentation and sparse scene flow to
classify objects as either background, moving, or potentially moving, thereby
ensuring that the system is able to model objects with the potential to
transition from static to dynamic, such as parked cars. Given camera poses
estimated from visual odometry, both the background and the (potentially)
moving objects are reconstructed separately by fusing the depth maps computed
from the stereo input. In addition to visual odometry, sparse scene flow is
also used to estimate the 3D motions of the detected moving objects, in order
to reconstruct them accurately. A map pruning technique is further developed to
improve reconstruction accuracy and reduce memory consumption, leading to
increased scalability. We evaluate our system thoroughly on the well-known
KITTI dataset. Our system is capable of running on a PC at approximately 2.5Hz,
with the primary bottleneck being the instance-aware semantic segmentation,
which is a limitation we hope to address in future work. The source code is
available from the project website (http://andreibarsan.github.io/dynslam).Comment: Presented at IEEE International Conference on Robotics and Automation
(ICRA), 201
Efficient 2D-3D Matching for Multi-Camera Visual Localization
Visual localization, i.e., determining the position and orientation of a
vehicle with respect to a map, is a key problem in autonomous driving. We
present a multicamera visual inertial localization algorithm for large scale
environments. To efficiently and effectively match features against a pre-built
global 3D map, we propose a prioritized feature matching scheme for
multi-camera systems. In contrast to existing works, designed for monocular
cameras, we (1) tailor the prioritization function to the multi-camera setup
and (2) run feature matching and pose estimation in parallel. This
significantly accelerates the matching and pose estimation stages and allows us
to dynamically adapt the matching efforts based on the surrounding environment.
In addition, we show how pose priors can be integrated into the localization
system to increase efficiency and robustness. Finally, we extend our algorithm
by fusing the absolute pose estimates with motion estimates from a multi-camera
visual inertial odometry pipeline (VIO). This results in a system that provides
reliable and drift-less pose estimation. Extensive experiments show that our
localization runs fast and robust under varying conditions, and that our
extended algorithm enables reliable real-time pose estimation.Comment: 7 pages, 5 figure
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