540 research outputs found
Fast, Accurate Thin-Structure Obstacle Detection for Autonomous Mobile Robots
Safety is paramount for mobile robotic platforms such as self-driving cars
and unmanned aerial vehicles. This work is devoted to a task that is
indispensable for safety yet was largely overlooked in the past -- detecting
obstacles that are of very thin structures, such as wires, cables and tree
branches. This is a challenging problem, as thin objects can be problematic for
active sensors such as lidar and sonar and even for stereo cameras. In this
work, we propose to use video sequences for thin obstacle detection. We
represent obstacles with edges in the video frames, and reconstruct them in 3D
using efficient edge-based visual odometry techniques. We provide both a
monocular camera solution and a stereo camera solution. The former incorporates
Inertial Measurement Unit (IMU) data to solve scale ambiguity, while the latter
enjoys a novel, purely vision-based solution. Experiments demonstrated that the
proposed methods are fast and able to detect thin obstacles robustly and
accurately under various conditions.Comment: Appeared at IEEE CVPR 2017 Workshop on Embedded Visio
An Underwater SLAM System using Sonar, Visual, Inertial, and Depth Sensor
This paper presents a novel tightly-coupled keyframe-based Simultaneous
Localization and Mapping (SLAM) system with loop-closing and relocalization
capabilities targeted for the underwater domain. Our previous work, SVIn,
augmented the state-of-the-art visual-inertial state estimation package OKVIS
to accommodate acoustic data from sonar in a non-linear optimization-based
framework. This paper addresses drift and loss of localization -- one of the
main problems affecting other packages in underwater domain -- by providing the
following main contributions: a robust initialization method to refine scale
using depth measurements, a fast preprocessing step to enhance the image
quality, and a real-time loop-closing and relocalization method using bag of
words (BoW). An additional contribution is the addition of depth measurements
from a pressure sensor to the tightly-coupled optimization formulation.
Experimental results on datasets collected with a custom-made underwater sensor
suite and an autonomous underwater vehicle from challenging underwater
environments with poor visibility demonstrate performance never achieved before
in terms of accuracy and robustness
Perception for detection and grasping
The final publication is available at link.springer.comThis research presents a methodology for the detection of the crawler used in the project AEROARMS. The approach consisted on using a two-step progressive strategy, going from rough detection and tracking, for approximation maneuvers, to an accurate positioning step based on fiducial markers. Two different methods are explained for the first step, one using efficient image segmentation approach; and the second one using Deep Learning techniques to detect the center of the crawler. The fiducial markers are used for precise localization of the crawler in a similar way as explained in earlier chapters. The methods can run in real-time.Peer ReviewedPostprint (author's final draft
Learning to Fly by Crashing
How do you learn to navigate an Unmanned Aerial Vehicle (UAV) and avoid
obstacles? One approach is to use a small dataset collected by human experts:
however, high capacity learning algorithms tend to overfit when trained with
little data. An alternative is to use simulation. But the gap between
simulation and real world remains large especially for perception problems. The
reason most research avoids using large-scale real data is the fear of crashes!
In this paper, we propose to bite the bullet and collect a dataset of crashes
itself! We build a drone whose sole purpose is to crash into objects: it
samples naive trajectories and crashes into random objects. We crash our drone
11,500 times to create one of the biggest UAV crash dataset. This dataset
captures the different ways in which a UAV can crash. We use all this negative
flying data in conjunction with positive data sampled from the same
trajectories to learn a simple yet powerful policy for UAV navigation. We show
that this simple self-supervised model is quite effective in navigating the UAV
even in extremely cluttered environments with dynamic obstacles including
humans. For supplementary video see: https://youtu.be/u151hJaGKU
Simultaneous Localization and Mapping (SLAM) on NAO
Simultaneous Localization and Mapping (SLAM) is a navigation and mapping method used by autonomous robots and moving vehicles. SLAM is mainly concerned with the problem of building a map in an unknown environment and concurrently navigating through the environment using the map. Localization is of utmost importance to allow the robot to keep track of its position with respect to the environment and the common use of odometry proves to be unreliable. SLAM has been proposed as a solution by previous research to provide more accurate localization and mapping on robots. This project involves the implementation of the SLAM algorithm in the humanoid robot NAO by Aldebaran Robotics. The SLAM technique will be implemented using vision from the single camera attached to the robot to map and localize the position of NAO in the environment. The result details the attempt to implement specifically the chosen algorithm, 1-Point RANSAC Inverse Depth EKF Monocular SLAM by Dr Javier Civera on the robot NAO. The algorithm is shown to perform well for smooth motions but on the humanoid NAO, the sudden changes in motion produces undesirable results.This study on SLAM will be useful as this technique can be widely used to allow mobile robots to map and navigate in areas which are deemed unsafe for humans
Dynamic Body VSLAM with Semantic Constraints
Image based reconstruction of urban environments is a challenging problem
that deals with optimization of large number of variables, and has several
sources of errors like the presence of dynamic objects. Since most large scale
approaches make the assumption of observing static scenes, dynamic objects are
relegated to the noise modeling section of such systems. This is an approach of
convenience since the RANSAC based framework used to compute most multiview
geometric quantities for static scenes naturally confine dynamic objects to the
class of outlier measurements. However, reconstructing dynamic objects along
with the static environment helps us get a complete picture of an urban
environment. Such understanding can then be used for important robotic tasks
like path planning for autonomous navigation, obstacle tracking and avoidance,
and other areas. In this paper, we propose a system for robust SLAM that works
in both static and dynamic environments. To overcome the challenge of dynamic
objects in the scene, we propose a new model to incorporate semantic
constraints into the reconstruction algorithm. While some of these constraints
are based on multi-layered dense CRFs trained over appearance as well as motion
cues, other proposed constraints can be expressed as additional terms in the
bundle adjustment optimization process that does iterative refinement of 3D
structure and camera / object motion trajectories. We show results on the
challenging KITTI urban dataset for accuracy of motion segmentation and
reconstruction of the trajectory and shape of moving objects relative to ground
truth. We are able to show average relative error reduction by a significant
amount for moving object trajectory reconstruction relative to state-of-the-art
methods like VISO 2, as well as standard bundle adjustment algorithms
TrackFlow: Multi-Object Tracking with Normalizing Flows
The field of multi-object tracking has recently seen a renewed interest in
the good old schema of tracking-by-detection, as its simplicity and strong
priors spare it from the complex design and painful babysitting of
tracking-by-attention approaches. In view of this, we aim at extending
tracking-by-detection to multi-modal settings, where a comprehensive cost has
to be computed from heterogeneous information e.g., 2D motion cues, visual
appearance, and pose estimates. More precisely, we follow a case study where a
rough estimate of 3D information is also available and must be merged with
other traditional metrics (e.g., the IoU). To achieve that, recent approaches
resort to either simple rules or complex heuristics to balance the contribution
of each cost. However, i) they require careful tuning of tailored
hyperparameters on a hold-out set, and ii) they imply these costs to be
independent, which does not hold in reality. We address these issues by
building upon an elegant probabilistic formulation, which considers the cost of
a candidate association as the negative log-likelihood yielded by a deep
density estimator, trained to model the conditional joint probability
distribution of correct associations. Our experiments, conducted on both
simulated and real benchmarks, show that our approach consistently enhances the
performance of several tracking-by-detection algorithms.Comment: Accepted at ICCV 202
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