460 research outputs found
HBST: A Hamming Distance embedding Binary Search Tree for Visual Place Recognition
Reliable and efficient Visual Place Recognition is a major building block of
modern SLAM systems. Leveraging on our prior work, in this paper we present a
Hamming Distance embedding Binary Search Tree (HBST) approach for binary
Descriptor Matching and Image Retrieval. HBST allows for descriptor Search and
Insertion in logarithmic time by exploiting particular properties of binary
Feature descriptors. We support the idea behind our search structure with a
thorough analysis on the exploited descriptor properties and their effects on
completeness and complexity of search and insertion. To validate our claims we
conducted comparative experiments for HBST and several state-of-the-art methods
on a broad range of publicly available datasets. HBST is available as a compact
open-source C++ header-only library.Comment: Submitted to IEEE Robotics and Automation Letters (RA-L) 2018 with
International Conference on Intelligent Robots and Systems (IROS) 2018
option, 8 pages, 10 figure
Adding Cues to Binary Feature Descriptors for Visual Place Recognition
In this paper we propose an approach to embed continuous and selector cues in
binary feature descriptors used for visual place recognition. The embedding is
achieved by extending each feature descriptor with a binary string that encodes
a cue and supports the Hamming distance metric. Augmenting the descriptors in
such a way has the advantage of being transparent to the procedure used to
compare them. We present two concrete applications of our methodology,
demonstrating the two considered types of cues. In addition to that, we
conducted on these applications a broad quantitative and comparative evaluation
covering five benchmark datasets and several state-of-the-art image retrieval
approaches in combination with various binary descriptor types.Comment: 8 pages, 8 figures, source: www.gitlab.com/srrg-software/srrg_bench,
submitted to ICRA 201
NICP: Dense normal based point cloud registration
In this paper we present a novel on-line method to recursively align point clouds. By considering each point together with the local features of the surface (normal and curvature), our method takes advantage of the 3D structure around the points for the determination of the data association between two clouds. The algorithm relies on a least squares formulation of the alignment problem, that minimizes an error metric depending on these surface characteristics. We named the approach Normal Iterative Closest Point (NICP in short). Extensive experiments on publicly available benchmark data show that NICP outperforms other state-of-the-art approaches
Fast and robust 3D feature extraction from sparse point clouds
Matching 3D point clouds, a critical operation in map building and localization, is difficult with Velodyne-type sensors due to the sparse and non-uniform point clouds that they produce. Standard methods from dense 3D point clouds are generally not effective. In this paper, we describe a featurebased approach using Principal Components Analysis (PCA) of neighborhoods of points, which results in mathematically principled line and plane features. The key contribution in this work is to show how this type of feature extraction can be done efficiently and robustly even on non-uniformly sampled point clouds. The resulting detector runs in real-time and can be easily tuned to have a low false positive rate, simplifying data association. We evaluate the performance of our algorithm on an autonomous car at the MCity Test Facility using a Velodyne HDL-32E, and we compare our results against the state-of-theart NARF keypoint detector. © 2016 IEEE
ProSLAM: Graph SLAM from a Programmer's Perspective
In this paper we present ProSLAM, a lightweight stereo visual SLAM system
designed with simplicity in mind. Our work stems from the experience gathered
by the authors while teaching SLAM to students and aims at providing a highly
modular system that can be easily implemented and understood. Rather than
focusing on the well known mathematical aspects of Stereo Visual SLAM, in this
work we highlight the data structures and the algorithmic aspects that one
needs to tackle during the design of such a system. We implemented ProSLAM
using the C++ programming language in combination with a minimal set of well
known used external libraries. In addition to an open source implementation, we
provide several code snippets that address the core aspects of our approach
directly in this paper. The results of a thorough validation performed on
standard benchmark datasets show that our approach achieves accuracy comparable
to state of the art methods, while requiring substantially less computational
resources.Comment: 8 pages, 8 figure
Simultaneous Parameter Calibration, Localization, and Mapping
The calibration parameters of a mobile robot play a substantial role in navigation tasks. Often these parameters are subject to variations that depend either on changes in the environment or on the load of the robot. In this paper, we propose an approach to simultaneously estimate a map of the environment, the position of the on-board sensors of the robot, and its kinematic parameters. Our method requires no prior knowledge about the environment and relies only on a rough initial guess of the parameters of the platform. The proposed approach estimates the parameters online and it is able to adapt to non-stationary changes of the configuration. We tested our approach in simulated environments and on a wide range of real-world data using different types of robotic platforms. (C) 2012 Taylor & Francis and The Robotics Society of Japa
Efficiently learning metric and topological maps with autonomous service robots
Models of the environment are needed for a wide range of robotic applications, from search and rescue to automated vacuum cleaning. Learning maps has therefore been a major research focus in the robotics community over the last decades. In general, one distinguishes between metric and topological maps. Metric maps model the environment based on grids or geometric representations whereas topological maps model the structure of the environment using a graph. The contribution of this paper is an approach that learns a metric as well as a topological map based on laser range data obtained with a mobile robot. Our approach consists of two steps. First, the robot solves the simultaneous localization and mapping problem using an efficient probabilistic filtering technique. In a second step, it acquires semantic information about the environment using machine learning techniques. This semantic information allows the robot to distinguish between different types of places like, e. g., corridors or rooms. This enables the robot to construct annotated metric as well as topological maps of the environment. All techniques have been implemented and thoroughly tested using real mobile robot in a variety of environments
Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection
Selective weeding is one of the key challenges in the field of agriculture
robotics. To accomplish this task, a farm robot should be able to accurately
detect plants and to distinguish them between crop and weeds. Most of the
promising state-of-the-art approaches make use of appearance-based models
trained on large annotated datasets. Unfortunately, creating large agricultural
datasets with pixel-level annotations is an extremely time consuming task,
actually penalizing the usage of data-driven techniques. In this paper, we face
this problem by proposing a novel and effective approach that aims to
dramatically minimize the human intervention needed to train the detection and
classification algorithms. The idea is to procedurally generate large synthetic
training datasets randomizing the key features of the target environment (i.e.,
crop and weed species, type of soil, light conditions). More specifically, by
tuning these model parameters, and exploiting a few real-world textures, it is
possible to render a large amount of realistic views of an artificial
agricultural scenario with no effort. The generated data can be directly used
to train the model or to supplement real-world images. We validate the proposed
methodology by using as testbed a modern deep learning based image segmentation
architecture. We compare the classification results obtained using both real
and synthetic images as training data. The reported results confirm the
effectiveness and the potentiality of our approach.Comment: To appear in IEEE/RSJ IROS 201
A General Framework for Flexible Multi-Cue Photometric Point Cloud Registration
The ability to build maps is a key functionality for the majority of mobile
robots. A central ingredient to most mapping systems is the registration or
alignment of the recorded sensor data. In this paper, we present a general
methodology for photometric registration that can deal with multiple different
cues. We provide examples for registering RGBD as well as 3D LIDAR data. In
contrast to popular point cloud registration approaches such as ICP our method
does not rely on explicit data association and exploits multiple modalities
such as raw range and image data streams. Color, depth, and normal information
are handled in an uniform manner and the registration is obtained by minimizing
the pixel-wise difference between two multi-channel images. We developed a
flexible and general framework and implemented our approach inside that
framework. We also released our implementation as open source C++ code. The
experiments show that our approach allows for an accurate registration of the
sensor data without requiring an explicit data association or model-specific
adaptations to datasets or sensors. Our approach exploits the different cues in
a natural and consistent way and the registration can be done at framerate for
a typical range or imaging sensor.Comment: 8 page
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