3,419 research outputs found
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
Monocular navigation for long-term autonomy
We present a reliable and robust monocular navigation system for an autonomous vehicle.
The proposed method is computationally efficient, needs off-the-shelf equipment only and does not require any additional infrastructure like radio beacons or GPS.
Contrary to traditional localization algorithms, which use advanced mathematical methods to determine vehicle position, our method uses a more practical approach.
In our case, an image-feature-based monocular vision technique determines only the heading of the vehicle while the vehicle's odometry is used to estimate the distance traveled.
We present a mathematical proof and experimental evidence indicating that the localization error of a robot guided by this principle is bound.
The experiments demonstrate that the method can cope with variable illumination, lighting deficiency and both short- and long-term environment changes.
This makes the method especially suitable for deployment in scenarios which require long-term autonomous operation
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