1,319 research outputs found
Place Categorization and Semantic Mapping on a Mobile Robot
In this paper we focus on the challenging problem of place categorization and
semantic mapping on a robot without environment-specific training. Motivated by
their ongoing success in various visual recognition tasks, we build our system
upon a state-of-the-art convolutional network. We overcome its closed-set
limitations by complementing the network with a series of one-vs-all
classifiers that can learn to recognize new semantic classes online. Prior
domain knowledge is incorporated by embedding the classification system into a
Bayesian filter framework that also ensures temporal coherence. We evaluate the
classification accuracy of the system on a robot that maps a variety of places
on our campus in real-time. We show how semantic information can boost robotic
object detection performance and how the semantic map can be used to modulate
the robot's behaviour during navigation tasks. The system is made available to
the community as a ROS module
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
Simultaneous Localization and Mapping (SLAM) for Autonomous Driving: Concept and Analysis
The Simultaneous Localization and Mapping (SLAM) technique has achieved astonishing progress over the last few decades and has generated considerable interest in the autonomous driving community. With its conceptual roots in navigation and mapping, SLAM outperforms some traditional positioning and localization techniques since it can support more reliable and robust localization, planning, and controlling to meet some key criteria for autonomous driving. In this study the authors first give an overview of the different SLAM implementation approaches and then discuss the applications of SLAM for autonomous driving with respect to different driving scenarios, vehicle system components and the characteristics of the SLAM approaches. The authors then discuss some challenging issues and current solutions when applying SLAM for autonomous driving. Some quantitative quality analysis means to evaluate the characteristics and performance of SLAM systems and to monitor the risk in SLAM estimation are reviewed. In addition, this study describes a real-world road test to demonstrate a multi-sensor-based modernized SLAM procedure for autonomous driving. The numerical results show that a high-precision 3D point cloud map can be generated by the SLAM procedure with the integration of Lidar and GNSS/INS. Online four–five cm accuracy localization solution can be achieved based on this pre-generated map and online Lidar scan matching with a tightly fused inertial system
Don't Look Back: Robustifying Place Categorization for Viewpoint- and Condition-Invariant Place Recognition
When a human drives a car along a road for the first time, they later
recognize where they are on the return journey typically without needing to
look in their rear-view mirror or turn around to look back, despite significant
viewpoint and appearance change. Such navigation capabilities are typically
attributed to our semantic visual understanding of the environment [1] beyond
geometry to recognizing the types of places we are passing through such as
"passing a shop on the left" or "moving through a forested area". Humans are in
effect using place categorization [2] to perform specific place recognition
even when the viewpoint is 180 degrees reversed. Recent advances in deep neural
networks have enabled high-performance semantic understanding of visual places
and scenes, opening up the possibility of emulating what humans do. In this
work, we develop a novel methodology for using the semantics-aware higher-order
layers of deep neural networks for recognizing specific places from within a
reference database. To further improve the robustness to appearance change, we
develop a descriptor normalization scheme that builds on the success of
normalization schemes for pure appearance-based techniques such as SeqSLAM [3].
Using two different datasets - one road-based, one pedestrian-based, we
evaluate the performance of the system in performing place recognition on
reverse traversals of a route with a limited field of view camera and no
turn-back-and-look behaviours, and compare to existing state-of-the-art
techniques and vanilla off-the-shelf features. The results demonstrate
significant improvements over the existing state of the art, especially for
extreme perceptual challenges that involve both great viewpoint change and
environmental appearance change. We also provide experimental analyses of the
contributions of the various system components.Comment: 9 pages, 11 figures, ICRA 201
Learning Deep NBNN Representations for Robust Place Categorization
This paper presents an approach for semantic place categorization using data
obtained from RGB cameras. Previous studies on visual place recognition and
classification have shown that, by considering features derived from
pre-trained Convolutional Neural Networks (CNNs) in combination with part-based
classification models, high recognition accuracy can be achieved, even in
presence of occlusions and severe viewpoint changes. Inspired by these works,
we propose to exploit local deep representations, representing images as set of
regions applying a Na\"{i}ve Bayes Nearest Neighbor (NBNN) model for image
classification. As opposed to previous methods where CNNs are merely used as
feature extractors, our approach seamlessly integrates the NBNN model into a
fully-convolutional neural network. Experimental results show that the proposed
algorithm outperforms previous methods based on pre-trained CNN models and
that, when employed in challenging robot place recognition tasks, it is robust
to occlusions, environmental and sensor changes
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