380 research outputs found
Probabilistic Surfel Fusion for Dense LiDAR Mapping
With the recent development of high-end LiDARs, more and more systems are
able to continuously map the environment while moving and producing spatially
redundant information. However, none of the previous approaches were able to
effectively exploit this redundancy in a dense LiDAR mapping problem. In this
paper, we present a new approach for dense LiDAR mapping using probabilistic
surfel fusion. The proposed system is capable of reconstructing a high-quality
dense surface element (surfel) map from spatially redundant multiple views.
This is achieved by a proposed probabilistic surfel fusion along with a
geometry considered data association. The proposed surfel data association
method considers surface resolution as well as high measurement uncertainty
along its beam direction which enables the mapping system to be able to control
surface resolution without introducing spatial digitization. The proposed
fusion method successfully suppresses the map noise level by considering
measurement noise caused by laser beam incident angle and depth distance in a
Bayesian filtering framework. Experimental results with simulated and real data
for the dense surfel mapping prove the ability of the proposed method to
accurately find the canonical form of the environment without further
post-processing.Comment: Accepted in Multiview Relationships in 3D Data 2017 (IEEE
International Conference on Computer Vision Workshops
Physics-based Simulation of Continuous-Wave LIDAR for Localization, Calibration and Tracking
Light Detection and Ranging (LIDAR) sensors play an important role in the
perception stack of autonomous robots, supplying mapping and localization
pipelines with depth measurements of the environment. While their accuracy
outperforms other types of depth sensors, such as stereo or time-of-flight
cameras, the accurate modeling of LIDAR sensors requires laborious manual
calibration that typically does not take into account the interaction of laser
light with different surface types, incidence angles and other phenomena that
significantly influence measurements. In this work, we introduce a physically
plausible model of a 2D continuous-wave LIDAR that accounts for the
surface-light interactions and simulates the measurement process in the Hokuyo
URG-04LX LIDAR. Through automatic differentiation, we employ gradient-based
optimization to estimate model parameters from real sensor measurements.Comment: Published at ICRA 202
Characterization of a RS-LiDAR for 3D Perception
High precision 3D LiDARs are still expensive and hard to acquire. This paper
presents the characteristics of RS-LiDAR, a model of low-cost LiDAR with
sufficient supplies, in comparison with VLP-16. The paper also provides a set
of evaluations to analyze the characterizations and performances of LiDARs
sensors. This work analyzes multiple properties, such as drift effects,
distance effects, color effects and sensor orientation effects, in the context
of 3D perception. By comparing with Velodyne LiDAR, we found RS-LiDAR as a
cheaper and acquirable substitute of VLP-16 with similar efficiency.Comment: For ICRA201
Characterization and calibration of multiple 2D laser scanners
This paper presents the comparative evaluation of multiple compact and lightweight 2D laser scanners for their possible backpack based scanning and mapping applications. These scanners include Hokuyo URG-04LX, Slamtec RPLidar A1-M8 and Hokuyo UTM- 30LX-EW scanners. Since the technical datasheets provide general information and limited working details, this research presents a thorough study on the performance of each scanner related explicitly to indoor mapping operations. A series of scanning experiments have been performed for the characterization of each scanner using statistical analysis. During the testing, all the scanning data has been recorded using Robot Operating System (ROS) and then computed in offline processing. In initial tests, each scanner's drift effect on range measurements has been tested and presented in the relevant section of the paper. In continuation, the effect of various scanning distances on measurement accuracy has been evaluated and discussed. Later the impact of various materials typically found in indoor vicinities and their respective properties of color and smoothness have been tested and provided in the paper. Finally, a Kalman Filtering based mathematical formulation has been utilized to calibrate each scanner and to reduce the measuring uncertainties as observed in various tests for each scanner
A comparison of A* and RRT* algorithms with dynamic and real time constraint scenarios for mobile robots
There is an increasing number of mobile robot applications. The demanding of the Industry 4.0 pushes the
robotic areas in the direction of the decision. The autonomous robots should actually decide the path according
to the dynamic environment. In some cases, time requirements must also be attended and require fast path
planning methods. This paper addresses a comparison between well-known path planning methods using a
realistic simulator that handles the dynamic properties of robot models including sensors. The methodology
is implemented in SimTwo that allows to compare the A* and RRT* algorithms in different scenarios with
dynamic and real time constraint scenarios.This work is financed by the ERDF – European
Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within
project POCI-01-0145-FEDER-006961, and by National Funds through the FCT – Fundac¸ao para ˜
a Ciencia e a Tecnologia (Portuguese Foundation ˆ
for Science and Technology) as part of project
UID/EEA/50014/2013.info:eu-repo/semantics/publishedVersio
Accumulator-free Hough Transform for Sequence Collinear Points
The perception, localization, and navigation of its environment are essential for autonomous mobile robots and vehicles. For that reason, a 2D Laser rangefinder sensor is used popularly in mobile robot applications to measure the origin of the robot to its surrounding objects. The measurement data generated by the sensor is transmitted to the controller, where the data is processed by one or multiple suitable algorithms in several steps to extract the desired information. Universal Hough Transform (UHT) is one of the appropriate and popular algorithms to extract the primitive geometry such as straight line, which later will be used in the further step of data processing. However, the UHT has high computational complexity and requires the so-called accumulator array, which is less suitable for real-time applications where a high speed and low complexity computation is highly demanded. In this study, an Accumulator-free Hough Transform (AfHT) is proposed to reduce the computational complexity and eliminate the need for the accumulator array. The proposed algorithm is validated using the measurement data from a 2D laser scanner and compared to the standard Hough Transform. As a result, the extracted value of AfHT shows a good agreement with that of UHT but with a significant reduction in the complexity of the computation and the need for computer memory
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