scholarly journals A Robust Localization System for Inspection Robots in Sewer Networks

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4946 ◽  
Author(s):  
David Alejo ◽  
Fernando Caballero ◽  
Luis Merino
Keyword(s):  
Cross Sections ◽  
Prior Information ◽  
Real Data ◽  
Localization System ◽  
Sewer Network ◽  
Localization And Mapping ◽  
Localization Errors ◽  
Robust Localization ◽  
Monte Carlo Localization ◽  
Inspection Robots

Sewers represent a very important infrastructure of cities whose state should be monitored periodically. However, the length of such infrastructure prevents sensor networks from being applicable. In this paper, we present a mobile platform (SIAR) designed to inspect the sewer network. It is capable of sensing gas concentrations and detecting failures in the network such as cracks and holes in the floor and walls or zones were the water is not flowing. These alarms should be precisely geo-localized to allow the operators performing the required correcting measures. To this end, this paper presents a robust localization system for global pose estimation on sewers. It makes use of prior information of the sewer network, including its topology, the different cross sections traversed and the position of some elements such as manholes. The system is based on a Monte Carlo Localization system that fuses wheel and RGB-D odometry for the prediction stage. The update step takes into account the sewer network topology for discarding wrong hypotheses. Additionally, the localization is further refined with novel updating steps proposed in this paper which are activated whenever a discrete element in the sewer network is detected or the relative orientation of the robot over the sewer gallery could be estimated. Each part of the system has been validated with real data obtained from the sewers of Barcelona. The whole system is able to obtain median localization errors in the order of one meter in all cases. Finally, the paper also includes comparisons with state-of-the-art Simultaneous Localization and Mapping (SLAM) systems that demonstrate the convenience of the approach.

scholarly journals Simultaneous Localization and Mapping for Inspection Robots in Water and Sewer Pipe Networks: A Review

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Jonathan M. Aitken ◽  
Mathew H. Evans ◽  
Rob Worley ◽  
Seamus Edwards ◽  
Rui Zhang ◽  
...  
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Sewer Pipe ◽  
Pipe Networks ◽  
Localization And Mapping ◽  
Inspection Robots

scholarly journals Vision-based SLAM system for MAVs in GPS-denied environments

2017 ◽  
Vol 9 (4) ◽  
pp. 283-296 ◽  
Author(s):  
Sarquis Urzua ◽  
Rodrigo Munguía ◽  
Antoni Grau
Keyword(s):  
Sensory Input ◽  
Low Cost ◽  
Real Data ◽  
Micro Aerial Vehicle ◽  
Localization And Mapping ◽  
Monocular Slam ◽  
Payload Capacity ◽  
The World ◽  
Aerial Vehicle ◽  
Metric Scale

Using a camera, a micro aerial vehicle (MAV) can perform visual-based navigation in periods or circumstances when GPS is not available, or when it is partially available. In this context, the monocular simultaneous localization and mapping (SLAM) methods represent an excellent alternative, due to several limitations regarding to the design of the platform, mobility and payload capacity that impose considerable restrictions on the available computational and sensing resources of the MAV. However, the use of monocular vision introduces some technical difficulties as the impossibility of directly recovering the metric scale of the world. In this work, a novel monocular SLAM system with application to MAVs is proposed. The sensory input is taken from a monocular downward facing camera, an ultrasonic range finder and a barometer. The proposed method is based on the theoretical findings obtained from an observability analysis. Experimental results with real data confirm those theoretical findings and show that the proposed method is capable of providing good results with low-cost hardware.

Computing Bonus–Malus Premiums under Partial Prior Information

2005 ◽  
Vol 11 (2) ◽  
pp. 361-374 ◽  
Author(s):  
E. Gómez-Déniz ◽  
L. Bermúdez ◽  
I. Morillo
Keyword(s):  
Bayesian Analysis ◽  
Structure Function ◽  
Loss Function ◽  
Prior Information ◽  
Real Data ◽  
Numerical Example ◽  
Robust Bayesian Analysis ◽  
Very High

ABSTRACTThe use of classical bonus–malus systems entails very high maluses and other problems which, during recent years, have been criticised by actuaries. To avoid these problems, new bonus–malus models have been developed. For instance, it is well known that the use of an exponential loss function reduces the differences between overcharges and undercharges, solving the problem of high maluses. In order to measure the sensitivity of the exponential bonus–malus system, and according to robust Bayesian analysis, we first model the structure function by specifying a subclass of the generalised moments class. We then examine the range of relativities for each prior. Finally, we illustrate our method with a numerical example based on real data.

scholarly journals Monocular SLAM for Visual Odometry: A Full Approach to the Delayed Inverse-Depth Feature Initialization Method

2012 ◽  
Vol 2012 ◽  
pp. 1-26 ◽  
Author(s):  
Rodrigo Munguía ◽  
Antoni Grau
Keyword(s):  
Sensory Input ◽  
A Priori ◽  
Real Data ◽  
Visual Odometry ◽  
Visual Features ◽  
Single Camera ◽  
Localization And Mapping ◽  
Monocular Slam ◽  
Freely Moving ◽  
Depth Feature

This paper describes in a detailed manner a method to implement a simultaneous localization and mapping (SLAM) system based on monocular vision for applications of visual odometry, appearance-based sensing, and emulation of range-bearing measurements. SLAM techniques are required to operate mobile robots ina prioriunknown environments using only on-board sensors to simultaneously build a map of their surroundings; this map will be needed for the robot to track its position. In this context, the 6-DOF (degree of freedom) monocular camera case (monocular SLAM) possibly represents the harder variant of SLAM. In monocular SLAM, a single camera, which is freely moving through its environment, represents the sole sensory input to the system. The method proposed in this paper is based on a technique called delayed inverse-depth feature initialization, which is intended to initialize new visual features on the system. In this work, detailed formulation, extended discussions, and experiments with real data are presented in order to validate and to show the performance of the proposal.

scholarly journals SISTEM NAVIGASI MOBILE ROBOT DALAM RUANGAN BERBASIS AUTONOMOUS NAVIGATION

2019 ◽  
Vol 4 (2) ◽  
pp. 78 ◽  
Author(s):  
Dwiky Erlangga ◽  
Endang D ◽  
Rosalia H S ◽  
Sunarto Sunarto ◽  
Kuat Rahardjo T.S ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Autonomous Navigation ◽  
Local Planning ◽  
Localization And Mapping ◽  
Monte Carlo Localization ◽  
Computer Based ◽  
Slam Algorithm ◽  
Main Components ◽  
Window Approach ◽  
Global Planning

<p><em>Autonomous navigation is absolutely necessary in mobile-robotic, which consists of four main components, namely: perception, localization, path-planning, and motion-control. Mobile robots create maps of space so that they can carry out commands to move from one place to another using the autonomous-navigation method. Map making using the Simultaneous-Localization-and-Mapping (SLAM) algorithm that processes data from the RGB-D camera sensor and bumper converted to laser-scan and point-cloud is used to obtain perception. While the wheel-encoder and gyroscope are used to obtain odometry data which is used to construct travel maps with the SLAM algorithm, gmapping and performing autonomous navigation. The system consists of three sub-systems, namely: sensors as inputs, single-board computers for processes, and actuators as movers. Autonomous-navigation is regulated through the navigation-stack using the Adaptive-Monte-Carlo-Localization (AMCL) algorithm for localization and global-planning, while the Dynamic-Window-Approach (DWA) algorithm with Robot-Operating-System-(ROS) for local -planning. The results of the test show the system can provide depth-data that is converted to laser-scan, bumper data, and odometry data to single-board-computer-based ROS so that mobile-controlled teleoperating robots from workstations can build 2-dimensional grid maps with total accuracy error rate of 0.987%. By using maps, data from sensors, and odometry the mobile-robot can perform autonomous-navigation consistently and be able to do path-replanning, avoid static obstacles and continue to do localization to reach the destination point.</em></p>

scholarly journals The Auto-Complete Graph: Merging and Mutual Correction of Sensor and Prior Maps for SLAM

Robotics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 40 ◽  
Author(s):  
Malcolm Mielle ◽  
Martin Magnusson ◽  
Achim J. Lilienthal
Keyword(s):  
Complete Graph ◽  
Prior Information ◽  
Simultaneous Localization And Mapping ◽  
Field Tests ◽  
Local Scale ◽  
Sensor Noise ◽  
Training Facility ◽  
Points Of Interest ◽  
Localization And Mapping ◽  
Scale Integration

Simultaneous Localization And Mapping (SLAM) usually assumes the robot starts without knowledge of the environment. While prior information, such as emergency maps or layout maps, is often available, integration is not trivial since such maps are often out of date and have uncertainty in local scale. Integration of prior map information is further complicated by sensor noise, drift in the measurements, and incorrect scan registrations in the sensor map. We present the Auto-Complete Graph (ACG), a graph-based SLAM method merging elements of sensor and prior maps into one consistent representation. After optimizing the ACG, the sensor map’s errors are corrected thanks to the prior map, while the sensor map corrects the local scale inaccuracies in the prior map. We provide three datasets with associated prior maps: two recorded in campus environments, and one from a fireman training facility. Our method handled up to 40% of noise in odometry, was robust to varying levels of details between the prior and the sensor map, and could correct local scale errors of the prior. In field tests with ACG, users indicated points of interest directly on the prior before exploration. We did not record failures in reaching them.

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