Indoor Navigation Services Research Articles

A fast and stable calibration method for VLP system without any geometric measurement

Abstract. Visible light positioning (VLP) is one of the most promising technologies for providing high-precision, low-cost indoor positioning and navigation services. However, the traditional calibration methods of VLP are complex and unfriendly to users, which hinders the large-scale commercial deployment of VLP systems. In this letter, a fast and stable calibration method for Lambert model in the received signal strength (RSS)-based VLP system is proposed, which dispenses with geometric measurement and greatly simplifies the calibration procedures. The proposed method calibrates the Lambert model through two steps, firstly using a ratio method to calibrate the Lambert order, and then estimating the constant term. The actual processes only require moving a robot equipped with a photo-detector (PD) along a rectangular trajectory once, and then the program will automatically estimate the required parameters by analyzing the RSS during this period. Experimental results show a good stability of the calibrated parameters, as well as a excellent distance measuring accuracy within 12 cm. And the entire processes, including signal collection and processing, only take a total of no more than four minutes.

Indoor hierarchy relation graph construction method based on RGB‐D

AbstractFine‐grained indoor navigation services require obstacle‐level indoor maps to support, but since indoor environments are affected by human activities, resulting in frequent changes in indoor spatial layouts, and indoor environments are easily affected by light and occlusion, the vast majority of indoor maps are at room level, limiting indoor obstacle‐level navigation path planning. To solve this problem, this paper proposes a hierarchy relation graph (HRG) construction method based on RGB‐D. Firstly, the semantic information extraction of indoor scenes and elements is realized by the output transformed PSPNet and YOLO V8 models, and the bounding box of each element is obtained based on YOLO V8. Then an algorithm for determining the hierarchical relationship of indoor elements is proposed, which calculates the correlation between the two elements from both plane and depth dimensions and constructs a HRG of indoor elements based on directed trees. Finally, comparative experiments are designed to validate the proposed method. Experiments showed that the proposed method can construct HRGs in a variety of scenes; the hierarchy relation detection rate is 88.28%; the accuracy of hierarchy relation determination is 73.44%; and the single‐scene HRG can be generated in 3.81 s.

Unified Travel Solutions: Bridging Outdoor Route Planning with Intelligent Indoor Navigation

Indoor navigation, an innovative service built on indoor localisation, is a game-changer for travellers. This paper introduces a unique multimodal, dynamic indoor navigation service for indoor spaces. The novelty of this service lies in its seamless integration with outdoor routers, paving the way for a comprehensive door-to-door trip planner. The service's indoor multimodality is a key focus, considering accessibility options profiling and incorporating limited vehicular paths (e.g., internal buses in airports). The service's indoor dynamism is another standout feature involving real-time monitoring of events within the navigation path. The integration with outdoor routers is a significant achievement, primarily through the establishment of common interconnection points (shared points where indoor and outdoor navigation systems can exchange data) and a common data format structure (a standardised way of representing and exchanging navigation data). The proposed navigation service was put to the test in three real deployments at Berlin Tegel (TXL), Berlin Schönefeld (SXF), and Palma International (PMI) airports. Users travelling between these cities experienced the system's rapid detection of mechanical problems (e.g., travellators or elevators out of order) and incidents (e.g., temporarily non-navigable areas). The service's integration with other travel assistants and services, such as evaluating waiting times at check-in counters and security checkpoints, provided more accurate estimations of indoor navigation travel time and helped avoid agglomeration. These successful real-world validations underscore the service's effectiveness and reliability. The findings indicate that this innovative service significantly improves the travel experience by enhancing the planning and scheduling of movements from origin to destination. The validation showed an increase in travel efficiency, reduced wait times, and better accessibility options for travellers, underscoring the practical benefits of the proposed door-to-door navigation system.

GM(1,1)-Based Weighted K-Nearest Neighbor Algorithm for Indoor Localization

Along with the IoT technology, the importance of indoor positioning is increasing, but the accuracy of the traditional fingerprint positioning algorithm is negatively affected by the complex indoor environment. This issue of low indoor spatial geolocation localization accuracy when the signal is collected away from the present stage occurs due to the signal instability of the iBeacon in the traditional fingerprint localization algorithm, which generates a variety of factors such as object blocking and reflection, multipath effect, etc., as well as the scarcity of reference fingerprint data points. In response, this study proposes an inverse distance-weighted optimization WKNN algorithm for indoor localization based on the GM(1,1) model. By implementing GM(1,1) model pre-process leveling, the original fingerprint library was reconstructed into a large-capacity fingerprint database using the inverse distance-weighted interpolation method. The local inverse distance-weighted interpolation was used for interpolation, combined with the WKNN algorithm to complete the coordinate solution in real time. This effectively solved the issue of low localization accuracy caused by the large fluctuation of the received signal strength (RSS) sampling measurement data and the existence of few reference fingerprint datapoints in the fingerprint database. The results show that this algorithm reduced the average positioning error by 5.9% compared with ordinary kriging (OK) interpolation leveling and reduced the average positioning error by 18.2% compared with the indoor spatial location accuracy of the original fingerprint database, which can effectively improve the positioning accuracy and provide technical support for indoor location and navigation services.

Structure-guided camera localization for indoor environments

Camera-based indoor localization is a fundamental aspect of indoor navigation, virtual reality, and location-based services. Deep learning methods have exhibited remarkable performance with low storage requirements and high efficiency. However, existing methods mainly derive features implicitly for pose regression without considering explicit structure information from images. This paper proposes that incorporating such information can improve the localization performance of learning-based approaches. We extract structure information from RGB images in the form of depth maps and edge maps and design two modules for depth-weighted and edge-weighted feature fusion. These modules are integrated into the pose regression network to enhance pose prediction. Furthermore, we employ a self-attention module for high-level feature extraction to augment the network capacity. Extensive experiments are conducted on the publicly available 7Scenes and 12Scenes datasets, and the results demonstrate that the proposed method achieves high localization performance, with an average positional error of 0.19m and 0.16m, respectively. The code for this work is available at https://github.com/lqing900205/structureLoc.

Novel cartographer using an OAK-D smart camera for indoor robots location and navigation

In recent years, service robots have been widely used in people’s daily life, and with the development of more and more intelligence, people put forward higher requirements for autonomous positioning and navigation functions of robots. Like outdoor navigation, indoor navigation also needs the support of navigation data. Although the indoor positioning and navigation scheme based on cameras, lidars and other sensors is gradually developing, due to the complexity of the indoor structure, manual production of indoor navigation data is time-consuming and laborious, and the efficiency is relatively low. In order to solve the problem of low productivity and improve the accuracy of robot automatic navigation, we added a new type of intelligent camera, called OpenCV AI kit or OAK-D, and proposed a method to automatically build data files that can be used for indoor navigation and location services using indoor 3D point cloud data. This intelligent camera performs neural reasoning on chips that do not use GPUs. It can also use stereo drills for depth estimation, and use 4K color camera images as input to run the neural network model. Python API can be called to realize real-time detection of indoor doors, windows and other static objects. The target detection technology uses an artificial intelligence camera, and the robot can well identify and accurately mark on the indoor map. In this paper, a high-performance indoor robot navigation system is developed, and multisensor fusion technology is designed. Environmental information is collected through artificial intelligent camera (OAK-D), laser lidar, and data fusion is carried out. In the experiment part of this paper,The static fusion map module is created based on the laser sensor information and the sensor information of the depth camera, the hierarchical dynamic cost map module is created in the real-time navigation, and the global positioning of the robot is realized by combining the word bag model and the laser point cloud matching. Then a software system is realized by integrating each module. The experiment proves that the system is practical and effective, and has practical value.

RadarPDR: Radar-Assisted Indoor Pedestrian Dead Reckoning

Pedestrian dead reckoning (PDR) is the critical component in indoor pedestrian tracking and navigation services. While most of the recent PDR solutions exploit in-built inertial sensors in smartphones for next step estimation, due to measurement errors and sensing drift, the accuracy of walking direction, step detection, and step length estimation cannot be guaranteed, leading to large accumulative tracking errors. In this paper, we propose a radar-assisted PDR scheme, called RadarPDR, which integrates a frequency-modulation continuous-wave (FMCW) radar to assist the inertial sensors-based PDR. We first establish a segmented wall distance calibration model to deal with the radar ranging noise caused by irregular indoor building layouts and fuse wall distance estimation with acceleration and azimuth signals measured by the inertial sensors of a smartphone. We also propose a hierarchical particle filter(PF) together with an extended Kalman filter for position and trajectory adjustment. Experiments have been conducted in practical indoor scenarios. Results demonstrate that the proposed RadarPDR is efficient and stable and outperforms the widely used inertial sensors-based PDR scheme.

Multidirectional Differential RSS Technique for Indoor Vehicle Navigation

While indoor vehicle navigation is an essential component in large-scale parking garages of major cities, technical limitations and challenging propagation environments considerably affect the accuracy of existing localization techniques. We propose practical and robust proximity and direction detection schemes of vehicles for indoor navigation services using low-cost beacons. Specifically, the proposed solution allows a handheld mobile device within a moving vehicle to autonomously detect its approximate position and moving direction by only observing received signal strength (RSS) values of beacon signals. The proposed approach essentially exploits the differential RSS technique over perpendicularly oriented directional beams to reduce the impact of uncontrolled factors, including the vehicle and the mobile device. A low-cost multidirectional beacon prototype is developed using Bluetooth technology. The localization performance is evaluated using 96 beacons in an underground parking garage within an area of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$394.8 \mathrm {m} \times 304.3 \mathrm {m}$ </tex-math></inline-formula> . Experimental results show that the 90th percentiles of the average proximity and direction detection errors are 0.8 and 1.64 m, respectively. Furthermore, our proposed scheme provides robust proximity and direction detection performance with various vehicles and mobile devices’ orientations.

TransFloor

Smart on-demand delivery services require accurate indoor localization to enhance the system-human synergy experience of couriers in complex multi-story malls and platform construction. Floor localization is an essential part of indoor positioning, which can provide floor/altitude data support for upper-level 3D indoor navigation services (e.g., delivery route planning) to improve delivery efficiency and optimize order dispatching strategies. We argue that due to label dependence and device dependence, the existing floor localization methods cannot be flexibly deployed on a large scale in numerous multi-story malls across the country, nor can they apply to all couriers/users on the platform. This paper proposes a novel self-evolving and user-transparent floor localization system named TransFloor, based on crowdsourcing delivery data (e.g., order status and sensors data) without additional label investment and specialized equipment constraints. TransFloor consists of an unsupervised barometer-based module--IOD-TKPD and an NLP-inspired Wi-Fi-based module--Wifi2Vec, and Self-Labeling is a perfect bridge between both to completely achieve label-free and device-independent floor positioning. In addition, TransFloor is designed as a lightweight plugin embedded into the platform without refactoring the existing architecture, and it has been deployed nationwide to adaptively launch real-time accurate 3D/floor positioning services for numerous crowdsourcing couriers. We evaluate TransFloor on real-world records from an instant delivery platform (involving 672,282 orders, 7,390 couriers, and 6,206 merchants in 388 malls during two months). It can achieve an average accuracy of 94.61% and demonstrate good robustness to device heterogeneity and adaptive durability, outperforming existing state-of-the-art methods. Crucially, it can effectively improve user satisfaction and reduce overdue delivery by providing accurate floor navigation information in complex multi-story malls. As a case study, the platform reduces erroneous order scheduling by 60% and overdue delivery by 2.7%, and increases delivery efficiency by reducing courier arrival time by 12.27 seconds accounting for 7.29%. We believe that the key ideas of TransFloor can be extended to other crowdsourcing scenarios for the public further.

Learn then match: A fast coarse-to-fine depth image-based indoor localization framework for dark environments via deep learning and keypoint-based geometry alignment

Image-based indoor localization provides fundamental support for applications such as indoor navigation, virtual reality, and location-based services. Most research focuses on developing methods in good lighting conditions via RGB images; while for low lighting situations, especially at night, RGB-based methods cannot perform well. Depth images are promising alternative in such conditions as they record geometrical information instead of texture information, making it possible to work in low lighting scenarios. Current depth image-based methods, either retrieval-based methods or 3D registration-based methods, are inefficient due to its high computation overhead, preventing the wide applications. To address this issue, we propose a fast coarse-to-fine localization framework for dark environment via deep learning and keypoint-based geometry alignment. In the coarse localization phase, we jointly perform the depth completion and pose regression to relieve the occlusion caused appearance variance in depth images. In the refinement phase, keypoints are used instead of whole depth image points under the ICP alignment framework to increase the localization efficiency. The keypoints are detected on depth feature maps weakly supervised with pose regression. The experiments on the open available 7Scenes dataset show that the proposed method obtain positional accuracy of 0.143 m and orientational accuracy of 5.275°in average and only cost 0.8s for a single depth image. The code for the proposed work is available at https://github.com/lqing900205/KeyPointDepthLocalization

Mobile phone indoor scene features recognition localization method based on semantic constraint of building map location anchor

Abstract Visual features play a key role in indoor positioning and navigation services as the main semantic information to help people understand the environment. However, insufficient semantic constraint information and mismatching localization without building map have hindered the ubiquitous application services. To address the problem, we propose a smartphone indoor scene features recognition localization method with building map semantic constraints. First, based on Geographic Information System and Building Information Modeling techniques, a geocoded entity library of building Map Location Anchor (MLA) is constructed, which is able to provide users with “immersive” meta-building-map and semantic anchor constraints for mobile phone positioning when map matching. Second, using the MYOLOv5s deep learning model improved on indoor location scenario, the nine types of ubiquitous anchor features in building scenes are recognized in real time by acquiring video frames from the smartphone camera. Lastly, the spatial locations of the ubiquitous indoor facilities obtained using smartphone video recognition are matched with the MLA P3P algorithm to achieve real-time positioning and navigation. The experimental results show that the MLA recognition accuracy of the improved MYOLOv5s is 97.2%, and the maximum localization error is within the range of 0.775 m and confined to the interval of 0.5 m after applying the Building Information Modeling based Positioning and Navigation road network step node constraint, which can effectively achieve high positioning accuracy in the building indoor scenarios with adequate MLA and road network constraint.

Can indoor navigation service incorporating signs support spatial learning?

Can indoor navigation service incorporating signs support spatial learning?

EzNavi: An Easy-to-Operate Indoor Navigation System Based on Pedestrian Dead Reckoning and Crowdsourced User Trajectories

Recently, researchers have paid attention to designing indoor navigation services for smartphone users. Conventional indoor navigation systems highly rely on well-known indoor information and prior training phase for localization, and thus it is time- and labor-consuming to bootstrap indoor navigation services. Without too much prior configurations, the proposed indoor navigation system, called ezNavi , utilizes trajectory information (reported by users) to generate indoor pathway and point of interests (POIs). The proposed system consists of a front-end mobile application (APP) and a back-end server. The mobile APP infers users’ walking trajectories according to sensory values from smartphones. The back-end server processes crowdsourced trajectories with the help of deployed Bluetooth low energy beacon devices, and then produces pathways of the indoor environment. After obtaining more trajectories, the ezNavi can further refine the derived pathways to provide more efficient guidance services for users. Our experiment and prototyping results reveal that the ezNavi can effectively derive users’ walking trajectories, produces indoor pathways, and indicates directions for users.

CamNav: a computer-vision indoor navigation system

We present CamNav, a vision-based navigation system that provides users with indoor navigation services. CamNav captures images in real time while the user is walking to recognize their current location. It does not require any installation of indoor localization devices. In this paper, we describe the techniques of our system that improve the recognition accuracy of an existing system that uses oriented FAST and rotated BRIEF (ORB) as part of its location-matching procedure. We employ multiscale local binary pattern (MSLBP) features to recognize places. We implement CamNav and conduct required experiments to compare the obtained accuracy when using ORB, the scale-invariant feature transform (SIFT), MSLBP features, and the combination of both ORB and SIFT features with MSLBP. A dataset composed of 42 classes was constructed for assessment. Each class contains 100 pictures designed for training one location and 24 pictures dedicated for testing. The evaluation results demonstrate that the place recognition accuracy while using MSLBP features is better than the accuracy when using SIFT features. The accuracy when using SIFT, MSLBP, and ORB features is 88.19%, 91.27%, and 96.33%, respectively. The overall accuracy of recognizing places increased to 93.55% and 97.52% after integrating MSLBP with SIFT with ORB, respectively.

IndoorWaze: A Crowdsourcing-Based Context-Aware Indoor Navigation System

Indoor navigation systems are very useful in large complex indoor environments such as shopping malls. Current systems focus on improving indoor localization accuracy and must be combined with an accurate labeled floor plan to provide usable indoor navigation services. Such labeled floor plans are often unavailable or involve a prohibitive cost to manually obtain. In this paper, we present IndoorWaze, a novel crowdsourcing-based context-aware indoor navigation system that can automatically generate an accurate context-aware floor plan with labeled indoor POIs for the first time in literature. IndoorWaze combines the Wi-Fi fingerprints of indoor walkers with the Wi-Fi fingerprints and POI labels provided by POI employees to produce a high-fidelity labeled floor plan. As a lightweight crowdsourcing-based system, IndoorWaze involves very little effort from indoor walkers and POI employees. We prototype IndoorWaze on Android smartphones and evaluate it in a large shopping mall. Our results show that IndoorWaze can generate a high-fidelity labeled floor plan, in which all the stores are correctly labeled and arranged, all the pathways and crossings are correctly shown, and the median estimation error for the store dimension is below 12%.

Construction of Hybrid Dual Radio Frequency RSSI (HDRF-RSSI) Fingerprint Database and Indoor Location Method.

Radio frequency communication technology has not only greatly improved public network service, but also developed a new technological route for indoor navigation service. However, there is a gap between the precision and accuracy of indoor navigation services provided by indoor navigation service and the expectation of the public. This study proposed a method for constructing a hybrid dual frequency received signal strength indicator (HDRF-RSSI) fingerprint library, which is different from the traditional RSSI fingerprint library constructing method in indoor space using 2.4G radio frequency (RF) under the same Wi-Fi infrastructure condition. The proposed method combined 2.4G RF and 5G RF on the same access point (AP) device to construct a HDRF-RSSI fingerprint library, thereby doubling the fingerprint dimension of each reference point (RP). Experimental results show that the feature discriminability of HDRF-RSSI fingerprinting is 18.1% higher than 2.4G RF RSSI fingerprinting. Moreover, the hybrid radio frequency fingerprinting model, training loss function, and location evaluation algorithm based on the machine learning method were designed, so as to avoid limitation that transmission point (TP) and AP must be visible in the positioning method. In order to verify the effect of the proposed HDRF-RSSI fingerprint library construction method and the location evaluation algorithm, dual RF RSSI fingerprint data was collected to construct a fingerprint library in the experimental scene, which was trained using the proposed method. Several comparative experiments were designed to compare the positioning performance indicators such as precision and accuracy. Experimental results demonstrate that compared with the existing machine learning method based on Wi-Fi 2.4G RF RSSI fingerprint, the machine learning method combining Wi-Fi 5G RF RSSI vector and the original 2.4G RF RSSI vector can effectively improve the precision and accuracy of indoor positioning of the smart phone.

Optimizing AP and Beacon Placement in WiFi and BLE hybrid localization

Nowadays, wireless communication techniques, such as WiFi, Bluetooth low energy (BLE), etc., have been pervasive in our daily lives, and not only provide convenient data transmission services, but also enable popular indoor positioning and navigation services. The placement of wireless infrastructures like WiFi access points (APs) and BLE beacons have significant influences on the performance of localization. More importantly, since APs and beacons are probably used for both network access and localization, it is necessary to take into account both coverage and localization when deploying APs and beacons. In addition, there still exist other critical challenges, including optimizing AP and beacon placement in WiFi and BLE hybrid localization and optimizing the placement of extra APs and/or beacons in an existing wireless network, which are essentially NP-complete. This paper tackles these problems of optimizing AP and beacon placement by proposing a heuristic differential evolution algorithm based on the widely used Cramer-Rao lower bound (CRLB). To be specific, the CRLB is leveraged as a metric for localization and meanwhile a coverage degree criterion is defined as a metric for coverage, both of which are incorporated into the evaluation function of the differential evolution algorithm. Furthermore, instead of using the ideal log distance path loss (LDPL) model, the more practical Motley-Keenan model is adopted to reflect the influences of obstacles that are widespread in indoor environments. On these grounds, a software is designed and implemented based on Geotools to optimize AP and beacon placement in an interactive GUI manner. Finally, extensive simulations and field experiments are conducted, and a thorough comparison confirms the efficiency and effectiveness of the proposed algorithm.

Cadastral data as a source for 3D indoor modelling

Despite the rapid development of indoor spatial data acquisition technology, there are currently no solutions that enable large-scale indoor spatial data acquisition due to several limiting factors that characterize the indoor space. This fact, together with the rapidly growing need for indoor models, is the main motivation for our research. The focus is on the study of the appropriateness of existing cadastral data for 3D indoor modelling. Within the study, a framework for 3D indoor modelling has been developed, comprising a chain of processes, starting from initial cadastral data and ending with the OGC IndoorGML compliant document. The process chain is divided into three parts, which are described individually and supported by UML activity diagrams. The Slovenian Building Cadastre data represents the basis for the framework design and data assessment. The IndoorGML standard is used for final outputs, as it provides a standardized data model for the representation and exchange of indoor spatial information designed for indoor navigation and location-based services. The data storage options using a spatially enabled database are presented for storing 2D and 3D geometries. The stored data enables fully automatic IndoorGML document generation on request, while also taking advantage of all spatial database functionalities. The proposed approach is software independent and can be implemented with various spatially enabled software packages. In addition to 3D indoor data modelling, the framework represents a comprehensive method for assessing the usability of input data for the purpose of 3D indoor modelling. The assessment is done for the case of the Slovenian Building Cadastre. The assessment of the cadastral data suitability for 3D indoor modelling can be used for decisions regarding future steps towards a multi-purpose 3D real property cadastre. The presented concept can be applied in many countries worldwide that have a similar condominium registration system.

An intelligent indoor positioning system based on pedestrian directional signage object detection: a case study of Taipei Main Station.

Indoor positioning technologies have gained great interest from both industry and academia. Variety of services and applications can be built based on the availability and accessibility of indoor positioning information, for example indoor navigation and various location-based services. Different approaches have been proposed to provide indoor positioning information to users, in which an underlying system infrastructure is usually assumed to be well deployed in advance to provide the position information to users. Among many others, one common strategy is to deploy a bunch of active sensor nodes, such as WiFi APs and Bluetooth transceivers, to the indoor environment to serve as reference landmarks. The user's current location can thus be obtained directly or indirectly according to the active sensor signals collected by the user. Different from conventional infrastructure-based approaches, which put additional sensor devices to the environment, we utilize available objects in the environment as location landmarks. Leveraging wildly available smartphone devices as customer premises equipment to the user and the cutting-edge deep-learning technology, we investigate the feasibility of an infrastructure-free intelligent indoor positioning system based on visual information only. The proposed scheme has been verified by a real case study, which is to provide indoor positioning information to users in Taipei Main Station, one of the busiest transportation stations in the world. We use available pedestrian directional signage as location landmarks, which include all of the 52 pedestrian directional signs in the testing area. The Google Objection Detection framework is applied for detection and recognition of the pedestrian directional sign. According to the experimental results, we have shown that the proposed scheme can achieve as high as 98% accuracy to successfully identify the 52 pedestrian directional signs for the three test data sets which include 6,341 test images totally. Detailed discussions of the system design and the experiments are also presented in the paper.

A Self-Adaptive Method of Multi-scale POI Visualization in Indoor Map

Abstract. As an important representation of LBS (Location Based Service), the indoor map provides users with facility location, indoor navigation, wayfinding and other necessary services. Compared with the common map, the indoor map has characteristics of detailed representation, 3D visualization, and real-time reaction. As far as POI (Point of Interest) representation is concerned, the common urban map just visualizes the location and the basic attribute in a simple way by relatively large scale representation. In the indoor map, however, the POI needs to represent the 3D location (level, longitude, latitude), the access path, and some detailed service information. Take the map of shopping mall as an example, traditionally, some services visualize restaurants that should have been distributed at different layers in a crowding way, with icons touching and overlapping with each other. Therefore, the LOD (level of detail) strategy needs to be applied to obtain adaptive representation for users with different objectives.To satisfy the requirements of the new indoor map about POI visualization, this study explores the multi-scale method in 3D indoor mapping. We develop an indoor map which is integrated into an outdoor base map and all the floors of the whole building are stacked vertically. In this way, the problem of icon clustering could be overcome to an extent, POI data are visualized vertically in indoor space, each floor can be taken out like opening a drawer in order to make sure that all the POIs can be seen clearly. We adopt a scale adaptive strategy to visualize POI data in the indoor map. At large scale, the general floor distribution indicating the main function of each floor and functional partition of each floor is represented by different colors. At medium scale, POI data are visualized by simple symbols which can represent basic attribute information. At small scale, detailed service information (e.g., opening hours, number of seats remaining, price range and user ratings of a restaurant) of a POI that users are particularly interested in is described by well-designed map symbols based on the combination of visual variables. In addition, our POI visualization method is user-adaptive. Different data, symbols or color schemes will be shown for users with different age group, gender or purpose of using map. Because this indoor map is adaptive to both scale and user, each scale of the map can provide different map users with decision support from a different perspective.