scholarly journals An Orthogonal Weighted Occupancy Likelihood Map with IMU-Aided Laser Scan Matching for 2D Indoor Mapping

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1742 ◽  
Author(s):  
Chuang Qian ◽  
Hongjuan Zhang ◽  
Jian Tang ◽  
Bijun Li ◽  
Hui Liu
Keyword(s):  
Laser Scanner ◽  
Location Based Services ◽  
Experimental Result ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Localization And Mapping ◽  
Map Generation ◽  
Representation Method ◽  
Rigid Body Transformation ◽  
Grid Based

An indoor map is a piece of infrastructure associated with location-based services. Simultaneous Localization and Mapping (SLAM)-based mobile mapping is an efficient method to construct an indoor map. This paper proposes an SLAM algorithm based on a laser scanner and an Inertial Measurement Unit (IMU) for 2D indoor mapping. A grid-based occupancy likelihood map is chosen as the map representation method and is built from all previous scans. Scan-to-map matching is utilized to find the optimal rigid-body transformation in order to avoid the accumulation of matching errors. Map generation and update are probabilistically motivated. According to the assumption that the orthogonal is the main feature of indoor environments, we propose a lightweight segment extraction method, based on the orthogonal blurred segments (OBS) method. Instead of calculating the parameters of segments, we give the scan points contained in blurred segments a greater weight during the construction of the grid-based occupancy likelihood map, which we call the orthogonal feature weighted occupancy likelihood map (OWOLM). The OWOLM enhances the occupancy likelihood map by fusing the orthogonal features. It can filter out noise scan points, produced by objects, such as glass cabinets and bookcases. Experiments were carried out in a library, which is a representative indoor environment, consisting of orthogonal features. The experimental result proves that, compared with the general occupancy likelihood map, the OWOLM can effectively reduce accumulated errors and construct a clearer indoor map.

A New Approach to Sonar Based Indoor Mapping Localization

2005 ◽  
Author(s):  
A. Meghdari ◽  
K. Kobravi ◽  
H. Safyallah ◽  
M. Moeeni ◽  
Y. Khatami ◽  
...  
Keyword(s):  
Efficient Algorithm ◽  
Robot Navigation ◽  
Laser Scanner ◽  
Indoor Environments ◽  
Scanner Data ◽  
Narrow Beam ◽  
New Approach ◽  
Vehicle Localization ◽  
Environment Map ◽  
Map Generation

Vehicle localization and environment mapping are the most essential parts of the robot navigation in unknown environments. Since the problem of localization in indoor environments is directly related to the problem of online map generation, in this paper a new and efficient algorithm for simultaneous localization and map generation is proposed and novel results for real environments are achieved. This new algorithm interprets and validates the raw sonar measurements in first step, and applies them to the environment map in the next step. There are various adjustable parameters which make the algorithm flexible for different sonar types. This algorithm is efficient and is robust to sonar failure; if sonar does not work properly data can be discarded. These abilities make the algorithm efficient for sonar navigation in flat environments even by poor sonar and odometers perception data. This algorithm has the ability of matching with various types of sonar and even to be used with laser scanner data, whenever each laser scanner data is treated as multiple sonar detections with narrow beam detection patterns.

scholarly journals LASER-BASED SLAM WITH EFFICIENT OCCUPANCY LIKELIHOOD MAP LEARNING FOR DYNAMIC INDOOR SCENES

2016 ◽  
Vol III-4 ◽  
pp. 119-126 ◽  
Author(s):  
Li Li ◽  
Jian Yao ◽  
Renping Xie ◽  
Jinge Tu ◽  
Chen Feng
Keyword(s):  
Learning Strategy ◽  
Multiple Scales ◽  
Laser Scanner ◽  
Location Based Services ◽  
Indoor Environments ◽  
Map Building ◽  
Scan Matching ◽  
Map Learning ◽  
Novel Approach ◽  
Indoor Scenes

Location-Based Services (LBS) have attracted growing attention in recent years, especially in indoor environments. The fundamental technique of LBS is the map building for unknown environments, this technique also named as simultaneous localization and mapping (SLAM) in robotic society. In this paper, we propose a novel approach for SLAMin dynamic indoor scenes based on a 2D laser scanner mounted on a mobile Unmanned Ground Vehicle (UGV) with the help of the grid-based occupancy likelihood map. Instead of applying scan matching in two adjacent scans, we propose to match current scan with the occupancy likelihood map learned from all previous scans in multiple scales to avoid the accumulation of matching errors. Due to that the acquisition of the points in a scan is sequential but not simultaneous, there unavoidably exists the scan distortion at different extents. To compensate the scan distortion caused by the motion of the UGV, we propose to integrate a velocity of a laser range finder (LRF) into the scan matching optimization framework. Besides, to reduce the effect of dynamic objects such as walking pedestrians often existed in indoor scenes as much as possible, we propose a new occupancy likelihood map learning strategy by increasing or decreasing the probability of each occupancy grid after each scan matching. Experimental results in several challenged indoor scenes demonstrate that our proposed approach is capable of providing high-precision SLAM results.

scholarly journals A Smartphone Indoor Positioning System Using Hybrid Localization Technology

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3702 ◽  
Author(s):  
Hui-Seon Gang ◽  
Jae-Young Pyun
Keyword(s):  
Indoor Positioning ◽  
Location Based Services ◽  
Measurement Unit ◽  
Service Needs ◽  
Positioning System ◽  
Indoor Environments ◽  
Experimental Conditions ◽  
Daily Lives ◽  
Location Services ◽  
Positioning Method

As smartphone built-in sensors, wireless technologies, and processor computing power become more advanced and global positioning system (GPS)-based positioning technologies are improving, location-based services (LBS) have become a part of our daily lives. At the same time, demand has grown for LBS applications in indoor environments, such as indoor path finding and navigation, marketing, entertainment, and location-based information retrieval. In this paper, we demonstrate the design and implementation of a smartphone-based indoor LBS system for location services consisting of smartphone applications and a server. The proposed indoor LBS system uses hybrid indoor positioning methods based on Bluetooth beacons, Geomagnetic field, Inertial Measurement Unit (IMU) sensors, and smartphone cameras and can be used for three types of indoor LBS applications. The performance of each positioning method demonstrates that our system retains the desired accuracy under experimental conditions. As these results illustrate that our system can maintain positioning accuracy to within 2 m 80% of the time, we believe our system can be a real solution for various indoor positioning service needs.

scholarly journals Cost-Effective Proximity Detection and Filtering of Unreliable Results Based on BLE RSSI and IMU Readings Using Smartphones for Indoor Environments

2021 ◽  
Author(s):  
Katarzyna Filus ◽  
Sławomir Nowak ◽  
Joanna Domańska ◽  
Jakub Duda
Keyword(s):  
Low Cost ◽  
Real Life ◽  
Great Majority ◽  
Cost Effective ◽  
Location Based Services ◽  
Measurement Unit ◽  
Learning Activity ◽  
External Information ◽  
Indoor Environments ◽  
Boosted Decision Trees

Abstract Indoor environments are a major challenge in the domain of location-based services due to the inability to use GPS. Currently, Bluetooth Low Energy has been the most commonly used technology for such services due to its low cost, low power consumption, ubiquitous availability in smartphones and the dependence of the signal strength on the distance between devices. The article proposes a system that detects the proximity of a moving object with respect to static points (anchors), evaluates the quality of this prediction and filters out the unreliable results based on custom metrics. We define three metrics: two matrics based on RSSI and Intertial Measurement Unit (IMU) readings and one joint metric. This way the filtering is based on both, the external information (RSSI) and the internal information (IMU). To process the IMU data, we use machine learning activity recognition models (we apply feature selection and compare three models and choose the best one-Gradient Boosted Decision Trees). The proposed system is flexible and can be easily customized. The great majority of operations can be conducted directly on smartphones. The solution is easy to implement, cost-efficient and can be deployed in real-life applications (MICE industry, museums, industry).

scholarly journals An Enhanced Pedestrian Visual-Inertial SLAM System Aided with Vanishing Point in Indoor Environments

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7428
Author(s):  
Wennan Chai ◽  
Chao Li ◽  
Mingyue Zhang ◽  
Zhen Sun ◽  
Hao Yuan ◽  
...  
Keyword(s):  
Measurement Errors ◽  
Inertial Navigation ◽  
Dead Reckoning ◽  
Optimization Method ◽  
Measurement Unit ◽  
Vanishing Point ◽  
Indoor Environments ◽  
Localization And Mapping ◽  
Loop Detection ◽  
Pedestrian Dead Reckoning

The visual-inertial simultaneous localization and mapping (SLAM) is a feasible indoor positioning system that combines the visual SLAM with inertial navigation. There are accumulated drift errors in inertial navigation due to the state propagation and the bias of the inertial measurement unit (IMU) sensor. The visual-inertial SLAM can correct the drift errors via loop detection and local pose optimization. However, if the trajectory is not a closed loop, the drift error might not be significantly reduced. This paper presents a novel pedestrian dead reckoning (PDR)-aided visual-inertial SLAM, taking advantage of the enhanced vanishing point (VP) observation. The VP is integrated into the visual-inertial SLAM as an external observation without drift error to correct the system drift error. Additionally, the estimated trajectory’s scale is affected by the IMU measurement errors in visual-inertial SLAM. Pedestrian dead reckoning (PDR) velocity is employed to constrain the double integration result of acceleration measurement from the IMU. Furthermore, to enhance the proposed system’s robustness and the positioning accuracy, the local optimization based on the sliding window and the global optimization based on the segmentation window are proposed. A series of experiments are conducted using the public ADVIO dataset and a self-collected dataset to compare the proposed system with the visual-inertial SLAM. Finally, the results demonstrate that the proposed optimization method can effectively correct the accumulated drift error in the proposed visual-inertial SLAM system.

scholarly journals LASER-BASED SLAM WITH EFFICIENT OCCUPANCY LIKELIHOOD MAP LEARNING FOR DYNAMIC INDOOR SCENES

2016 ◽  
Vol III-4 ◽  
pp. 119-126
Author(s):  
Li Li ◽  
Jian Yao ◽  
Renping Xie ◽  
Jinge Tu ◽  
Chen Feng
Keyword(s):  
Learning Strategy ◽  
Multiple Scales ◽  
Laser Scanner ◽  
Location Based Services ◽  
Indoor Environments ◽  
Map Building ◽  
Scan Matching ◽  
Map Learning ◽  
Novel Approach ◽  
Indoor Scenes

Location-Based Services (LBS) have attracted growing attention in recent years, especially in indoor environments. The fundamental technique of LBS is the map building for unknown environments, this technique also named as simultaneous localization and mapping (SLAM) in robotic society. In this paper, we propose a novel approach for SLAMin dynamic indoor scenes based on a 2D laser scanner mounted on a mobile Unmanned Ground Vehicle (UGV) with the help of the grid-based occupancy likelihood map. Instead of applying scan matching in two adjacent scans, we propose to match current scan with the occupancy likelihood map learned from all previous scans in multiple scales to avoid the accumulation of matching errors. Due to that the acquisition of the points in a scan is sequential but not simultaneous, there unavoidably exists the scan distortion at different extents. To compensate the scan distortion caused by the motion of the UGV, we propose to integrate a velocity of a laser range finder (LRF) into the scan matching optimization framework. Besides, to reduce the effect of dynamic objects such as walking pedestrians often existed in indoor scenes as much as possible, we propose a new occupancy likelihood map learning strategy by increasing or decreasing the probability of each occupancy grid after each scan matching. Experimental results in several challenged indoor scenes demonstrate that our proposed approach is capable of providing high-precision SLAM results.

scholarly journals LINE SEGMENTATION OF 2D LASER SCANNER POINT CLOUDS FOR INDOOR SLAM BASED ON A RANGE OF RESIDUALS

2017 ◽  
Vol IV-2/W4 ◽  
pp. 363-369 ◽  
Author(s):  
M. Peter ◽  
S. R. U. N. Jafri ◽  
G. Vosselman
Keyword(s):  
Laser Scanning ◽  
Degrees Of Freedom ◽  
Large Scale ◽  
Laser Scanner ◽  
Point Clouds ◽  
Indoor Environments ◽  
Six Degrees Of Freedom ◽  
Localization And Mapping ◽  
Feature Based ◽  
Definition Of

Indoor mobile laser scanning (IMLS) based on the Simultaneous Localization and Mapping (SLAM) principle proves to be the preferred method to acquire data of indoor environments at a large scale. In previous work, we proposed a backpack IMLS system containing three 2D laser scanners and an according SLAM approach. The feature-based SLAM approach solves all six degrees of freedom simultaneously and builds on the association of lines to planes. Because of the iterative character of the SLAM process, the quality and reliability of the segmentation of linear segments in the scanlines plays a crucial role in the quality of the derived poses and consequently the point clouds. The orientations of the lines resulting from the segmentation can be influenced negatively by narrow objects which are nearly coplanar with walls (like e.g. doors) which will cause the line to be tilted if those objects are not detected as separate segments. State-of-the-art methods from the robotics domain like Iterative End Point Fit and Line Tracking were found to not handle such situations well. Thus, we describe a novel segmentation method based on the comparison of a range of residuals to a range of thresholds. For the definition of the thresholds we employ the fact that the expected value for the average of residuals of <i>n</i> points with respect to the line is <i>σ</i>&amp;thinsp;/&amp;thinsp;&amp;radic;<i>n</i>. Our method, as shown by the experiments and the comparison to other methods, is able to deliver more accurate results than the two approaches it was tested against.

Feature Based Mapping Procedure with Application on Simultaneous Localization and Mapping (SLAM)

2010 ◽  
Vol 166-167 ◽  
pp. 265-270
Author(s):  
Razvan Luca ◽  
Fritz Tröster ◽  
Robert Gall ◽  
Carmen Simion
Keyword(s):  
Autonomous Navigation ◽  
Autonomous Vehicle ◽  
Laser Scanner ◽  
Relevant Information ◽  
Process Data ◽  
Mapping Procedure ◽  
Localization And Mapping ◽  
Laser Scanners ◽  
Map Generation ◽  
Feature Based

We are presenting a feature based mapping procedure applied on data reduction to the relevant information used for autonomous navigation. The proceeding is based on the evaluation of the environment using a SICK LD laser scanner. We assume that laser scanners have the advantage of producing reliable data with well understood characteristics for map generation. By implementing evolutive algorithms we process data into lines representing edges of the surrounding objects and create a simplified representation of the environment (feature based). Because of the dynamic generation and evolution of the map, during the movement of the autonomous vehicle we are considering of merging and fitting the data by applying a shape correlation. The goal of our project defines the capability of a fully autonomous vehicle to safely drive through the environment until reaching the standard parking lots and complete autonomous parking procedures.

scholarly journals INVESTIGATION OF GEOMETRIC PERFORMANCE OF AN INDOOR MOBILE MAPPING SYSTEM

2018 ◽  
Vol XLII-2 ◽  
pp. 637-642 ◽  
Author(s):  
M. Maboudi ◽  
D. Bánhidi ◽  
M. Gerke
Keyword(s):  
Point Clouds ◽  
Location Based Services ◽  
Indoor Environments ◽  
Mobile Mapping ◽  
Destructive Effect ◽  
Localization And Mapping ◽  
Fast Solution ◽  
3D Information ◽  
Better Than

Up-to-date and reliable 3D information of indoor environments is a prerequisite for many location- based services. One possibility to capture the necessary 3D data is to make use of Mobile Mapping Systems (MMSs) which rely for instance on SLAM (simultaneous localization and mapping). In most indoor environments, MMSs are by far faster than classic static systems. Moreover, they might deliver the point clouds with higher degree of completeness. In this paper, the geometric quality of point clouds of a state-of-the-art MMS (Viametris iMS3D) is investigated. In order to quantify the quality of iMS3D MMS, four different evaluation strategies namely cloud to cloud, point to plane, target to target and model based evaluation are employed. We conclude that the measurement accuracies are better than 1&amp;thinsp;cm and the precision of the point clouds are better than 3&amp;thinsp;cm in our experiments. For indoor mapping applications with few centimeters accuracy, the system offers a very fast solution. Moreover, as a nature of the current SLAM-based approaches, trajectory loop should be closed, but in some practical situations, closing the local trajectory loop might not be always possible. Our observation reveals that performing continuous repeated scanning could decrease the destructive effect of local unclosed loops.

scholarly journals RESEARCH ON SEMANTIC-ASSISTED SLAM IN COMPLEX DYNAMIC INDOOR ENVIRONMENT

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 353-359
Author(s):  
C. Li ◽  
Z. Kang ◽  
J. Yang ◽  
F. Li ◽  
Y. Wang
Keyword(s):  
Indoor Environment ◽  
Moving Objects ◽  
Solution Process ◽  
Dynamic Environments ◽  
Satellite System ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Feature Points ◽  
Localization And Mapping ◽  
Object Area

Abstract. Visual Simultaneous Localization and Mapping (SLAM) systems have been widely investigated in response to requirements, since the traditional positioning technology, such as Global Navigation Satellite System (GNSS), cannot accomplish tasks in restricted environments. However, traditional SLAM methods which are mostly based on point feature tracking, usually fail in harsh environments. Previous works have proven that insufficient feature points caused by missing textures, feature mismatches caused by too fast camera movements, and abrupt illumination changes will eventually cause state estimation to fail. And meanwhile, pedestrians are unavoidable, which introduces fake feature associations, thus violating the strict assumption that the unknown environment is static in SLAM. In order to ensure how our system copes with the huge challenges brought by these factors in a complex indoor environment, this paper proposes a semantic-assisted Visual Inertial Odometer (VIO) system towards low-textured scenes and highly dynamic environments. The trained U-net will be used to detect moving objects. Then all feature points in the dynamic object area need to be eliminated, so as to avoid moving objects to participate in the pose solution process and improve robustness in dynamic environments. Finally, the constraints of inertial measurement unit (IMU) are added for low-textured environments. To evaluate the performance of the proposed method, experiments were conducted on the EuRoC and TUM public dataset, and the results demonstrate that the performance of our approach is robust in complex indoor environments.

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