Visual SLAM in dynamic environments based on object detection

A great number of visual simultaneous localization and mapping(VSLAM)systems need to assume static features in the environment.However,moving objects can vastly impair the performance of a VSLAM system which relies on the static-world assumption.To cope with this challenging topic,a real-time and robust VSLAM system based on ORB-SLAM2 for dynamic environments was proposed.To reduce the influence of dynamic content,we incorporate the deep-learning-based object detection method in the visual odometry,then the dynamic object probability model is added to raise the efficiency of object detection deep neural network and enhance the real-time performance of our system.Experiment with both on the TUM and KITTI benchmark dataset,as well as in a real-world environment,the results clarify that our method can significantly reduce the tracking error or drift,enhance the robustness,accuracy and stability of the VSLAM system in dynamic scenes.

Adaptive key SURF feature extraction and application in unmanned vehicle dynamic object recognition

A new method based on adaptive Hessian matrix threshold of finding key SRUF （ speeded up robust features） features is proposed and is applied to an unmanned vehicle for its dynamic object recognition and guided navigation. First, the object recognition algorithm based on SURF feature matching for unmanned vehicle guided navigation is introduced. Then, the standard local invariant feature extraction algorithm SRUF is analyzed, the Hessian Metrix is especially discussed, and a method of adaptive Hessian threshold is proposed which is based on correct matching point pairs threshold feedback under a close loop frame. At last, different dynamic object recognition experi- ments under different weather light conditions are discussed. The experimental result shows that the key SURF feature abstract algorithm and the dynamic object recognition method can be used for un- manned vehicle systems.

A Dynamic Object Behavior Model and Implementation Based on Computational Reflection

A dynamic object behavior model based on computational reflection is proposed. This model consists of function level and meta level, the meta objects in meta level manage the base objects and behaviors in function level, including dynamic binding and unbinding of base object and behavior. We implement this model with RoleJava Language,which is our self linguistic extension of the Java Language.Meta Objects are generated automatically at compile\|time,this makes the reflecton mechanism transparent to programmers.Finally an example applying this model to a banking system is presented.

An Intelligent Recommendation System for Real Estate Commodity

Most real estate agents develop new objects by visiting unfamiliar clients, distributing leaflets, or browsing other real estate trading website platforms,whereas consumers often rely on websites to search and compare prices when purchasing real property. In addition to being time consuming, this search processrenders it difficult for agents and consumers to understand the status changes ofobjects. In this study, Python is used to write web crawler and image recognitionprograms to capture object information from the web pages of real estate agents;perform data screening, arranging, and cleaning;compare the text of real estateobject information;as well as integrate and use the convolutional neural networkof a deep learning algorithm to implement image recognition. In this study, dataare acquired from two business-to-consumer real estate agency networks, i.e., theSinyi real estate agent and the Yungching real estate agent, and one consumer-toconsumer real estate agency platform, i.e., the, FiveNineOne real estate agent. Theresults indicate that text mining can reveal the similarities and differences betweenthe objects, list the number of days that the object has been available for sale onthe website, and provide the price fluctuations and fluctuation times during thesales period. In addition, 213,325 object amplification images are used as a database for training using deep learning algorithms, and the maximum image recognition accuracy achieved is 95%. The dynamic recommendation system for realestate objects constructed by combining text mining and image recognition systems enables developers in the real estate industry to understand the differencesbetween their commodities and other businesses in approximately 2 min, as wellas rapidly determine developable objects via comparison results provided by thesystem. Meanwhile, consumers require less time in searching and comparingprices after they have understood the commodity dynamic information, therebyallowing them to use the most efficient approach to purchase real estate objectsof their interest.

Researches on Cartographic Database-Based Interactive Three-Dimensional Topographic Map

With the development of computer graphics, the three-dimensional (3D) visualization brings new technological revolution to the traditional cartography. Therefore, the topographic 3D-map emerges to adapt to this technological revolution, and the applications of topographic 3D-map are spread rapidly to other relevant fields due to its incomparable advantage. The researches on digital map and the construction of map database offer strong technical support and abundant data source for this new technology, so the research and development of topographic 3D-map will receive greater concern. The basic data of the topographic 3D-map are rooted mainly in digital map and its basic model is derived from digital elevation model (DEM) and 3D-models of other DEM-based geographic features. In view of the potential enormous data and the complexity of geographic features, the dynamic representation of geographic information becomes the focus of the research of topographic 3D-map and also the prerequisite condition of 3D query and analysis. In addition to the equipment of hardware that are restraining, to a certain extent, the 3D representation, the data organization structure of geographic information will be the core problem of research on 3D-map. Level of detail (LOD), space partitioning, dynamic object loading (DOL) and object culling are core technologies of the dynamic 3D representation. The object- selection, attribute-query and model-editing are important functions and interaction tools for users with 3D-maps provided by topographic 3D-map system, all of which are based on the data structure of the 3D-model. This paper discusses the basic theories, concepts and cardinal principles of topographic 3D-map, expounds the basic way to organize the scene hierarchy of topographic 3D-map based on the node mechanism and studies the dynamic representation technologies of topographic 3D-map based on LOD, space partitioning, DOL and object culling. Moreover, such interactive operation functions are explored, in this paper, as spatial query, scene editing and management of topographic 3D-map. Finally, this paper describes briefly the applications of topographic 3D-map in its related fields.

‘It Really Represents…’: Reflexions on the Object in Peirce’s Hexadic Sign-System

This paper compares the manner in which C.S.Peirce’s late semiotic theory deals with the problems posed by the dynamic object and the interpretation of signs with that of the earlier,better-known system of 1903.It shows how non-specialist interpreters of signs are often led to suggest that there is more to,for example,a painting than the summation of the lines,forms and colours to be found on the canvas,in other words,to suggest that the sign’s dynamic object may be quite different from its representation as the sign’s immediate object.Now the 1903ten-class system is composed of the sign and two relational criteria,the fi rst of which being the sign’s mode of representation,which is no other than the relation holding between the sign and what,at the time,was defi ned simply as its object.This means that the object itself is not a relevant feature of the system,and therefore this latter,even when the mode of representation is associated with Peirce’s hypoicons,is unable to account for discrepancies between what observers interpret as the sign’s object and the way the sign represents it.One explanation for this discrepancy can be found in Peirce’s late,hexadic conception of semiosis,which,in 1908,explicitly integrated immediate and dynamic objects and a typology of which the criteria were not the earlier categories but three universes of experience by means of which Peirce was able to expand considerably the inventory of the sorts of entities qualifying as a sign’s dynamic object.The paper draws on these advances to show how immediate and dynamic objects can diverge,and how different interpreters come to widely differing conclusions concerning the objects of signs and how they are interpreted.

Structures of GMC W37

We carried out observations toward the giant molecular cloud W 37 with the J = 1 - 0 transitions of 12CO, 13CO and C180 using the 13.7 m single-dish telescope at the Delingha station of Purple Mountain Observatory. Based on these CO lines, we calculated the column densities and cloud masses for molecular clouds with radial velocities around ＋20 km s-1. The gas mass of W 37, calculated from 13CO emission, is 1.7 × 10^5 M, above the criterion to be considered a giant molecular cloud. The dense ridge of W 37 is a dense filament, which is supercritical in terms of linear mass ratio. Dense clumps found by C180 emission are aligned along the dense ridge at regular intervals of about 2.8 pc, similar to the clump separation caused by large-scale ＇sausage instability＇. We confirm the identification of the giant molecular filament （GMF） G 18.0-16.8 and find a new giant filament, G 16.5-15.8, located ~ 0.7° to the west of G 18.0-16.8. Both GMFs are not gravitationally bound, as indicated by their low linear mass ratio （- 80 M pc-l）. We compared the gas temperature map with the dust temperature map from Herschel images, and found similar structures. The spatial distributions of class I objects and the dense clumps are reminiscent of triggered star formation occurring in the northwestern part of W 37, which is close to NGC 6611.

High-speed three-dimensional shape measurement with inner shifting-phase fringe projection profilometry

Fringe projection profilometry(FPP)has been extensively studied in the field of three-dimensional(3D)measurement.Although FPP always uses high-frequency fringes to ensure high measurement accuracy,too many patterns are projected to unwrap the phase,which affects the speed of 3D reconstruction.We propose a high-speed 3D shape measurement method using only three high-frequency inner shifting-phase patterns(70 periods),which satisfies both high precision and high measuring speed requirements.Besides,our proposed method obtains the wrapped phase and the fringe order simultaneously without any other information and constraints.The proposed method has successfully reconstructed moving objects with high speed at the camera's full frame rate(1700 frames per second).