32,489 research outputs found
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
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 exploration of unknown indoor environments using a team of mobile robots
Whenever multiple robots have to solve a common task, they need to coordinate their actions to carry out the task efficiently and to avoid interferences between individual robots. This is especially the case when considering the problem of exploring an unknown environment with a team of mobile robots. To achieve efficient terrain coverage with the sensors of the robots, one first needs to identify unknown areas in the environment. Second, one has to assign target locations to the individual robots so that they gather new and relevant information about the environment with their sensors. This assignment should lead to a distribution of the robots over the environment in a way that they avoid redundant work and do not interfere with each other by, for example, blocking their paths. In this paper, we address the problem of efficiently coordinating a large team of mobile robots. To better distribute the robots over the environment and to avoid redundant work, we take into account the type of place a potential target is located in (e.g., a corridor or a room). This knowledge allows us to improve the distribution of robots over the environment compared to approaches lacking this capability. To autonomously determine the type of a place, we apply a classifier learned using the AdaBoost algorithm. The resulting classifier takes laser range data as input and is able to classify the current location with high accuracy. We additionally use a hidden Markov model to consider the spatial dependencies between nearby locations. Our approach to incorporate the information about the type of places in the assignment process has been implemented and tested in different environments. The experiments illustrate that our system effectively distributes the robots over the environment and allows them to accomplish their mission faster compared to approaches that ignore the place labels
- …