Robot Visual Simultaneous Localization and Mapping in Dynamic Environments

Wang, Yin-Tien; Chi, C.V.; Hung, Shiun-Kai

doi:10.1166/asl.2012.2448

Cited by 5 publications

(3 citation statements)

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“…Their method requires a significant amount of computing resources for background modeling and spatiotemporal feature extraction, so it may face some challenges in practical applications. In another paper [38], an MOD algorithm was designed using the spatial geometric constraint of the stationary landmarks in the environment. Based on the MOD algorithm, the moving objects could be discriminated from the stationary landmarks.…”

Section: Related Workmentioning

confidence: 99%

Stereo SLAM in Dynamic Environments Using Semantic Segmentation

Sun

et al. 2023

Electronics

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As we all know, many dynamic objects appear almost continuously in the real world that are immensely capable of impairing the performance of the majority of vision-based SLAM systems based on the static-world assumption. In order to improve the robustness and accuracy of visual SLAM in high-dynamic environments, a real-time and robust stereo SLAM system for dynamic scenes was proposed. To weaken the influence of dynamic content, the moving-object detection method was put forward in our visual odometry, and then the semantic segmentation network was combined in our stereo SLAM to extract pixel-level contours of dynamic objects. Then, the influences of dynamic objects were significantly weakened and the performance of our system increased markedly in dynamic, complex, and crowed city spaces. Following experiments with both the KITTI Odometry dataset and in a real-life scene, the results showed that our method could dramatically decrease the tracking error or drift, and improve the robustness and stability of our stereo SLAM in high dynamic outdoor scenarios.

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Section: Related Workmentioning

confidence: 99%

Stereo SLAM in Dynamic Environments Using Semantic Segmentation

Sun

et al. 2023

Electronics

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“…Equations ( 5)-( 7) are used to determine the 3D coordinates of the ith image feature expressed in the left-camera frame (Wang et al, 2012) as:…”

Section: Binocular Vision Sensor Systemmentioning

confidence: 99%

Robot mapping using local invariant feature detectors

Wang

Chi

Feng

2014

Engineering Computations

Self Cite

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Purpose -To build a persistent map with visual landmarks is one of the most important steps for implementing the visual simultaneous localization and mapping (SLAM). The corner detector is a common method utilized to detect visual landmarks for constructing a map of the environment. However, due to the scale-variant characteristic of corner detection, extensive computational cost is needed to recover the scale and orientation of corner features in SLAM tasks. The purpose of this paper is to build the map using a local invariant feature detector, namely speeded-up robust features (SURF), to detect scale-and orientation-invariant features as well as provide a robust representation of visual landmarks for SLAM. Design/methodology/approach -SURF are scale-and orientation-invariant features which have higher repeatability than that obtained by other detection methods. Furthermore, SURF algorithms have better processing speed than other scale-invariant detection method. The procedures of detection, description and matching of regular SURF algorithms are modified in this paper in order to provide a robust representation of visual landmarks in SLAM. The sparse representation is also used to describe the environmental map and to reduce the computational complexity in state estimation using extended Kalman filter (EKF). Furthermore, the effective procedures of data association and map management for SURF features in SLAM are also designed to improve the accuracy of robot state estimation. Findings -Experimental works were carried out on an actual system with binocular vision sensors to prove the feasibility and effectiveness of the proposed algorithms. EKF SLAM with the modified SURF algorithms was applied in the experiments including the evaluation of accurate state estimation as well as the implementation of large-area SLAM. The performance of the modified SURF algorithms was compared with those obtained by regular SURF algorithms. The results show that the SURF with less-dimensional descriptors is the most suitable representation of visual landmarks. Meanwhile, the integrated system is successfully validated to fulfill the capabilities of visual SLAM system. Originality/value -The contribution of this paper is the novel approach to overcome the problem of recovering the scale and orientation of visual landmarks in SLAM tasks. This research also extends the usability of local invariant feature detectors in SLAM tasks by utilizing its robust representation of visual landmarks. Furthermore, data association and map management designed for SURF-based mapping in this paper also give another perspective for improving the robustness of SLAM systems. IntroductionTo build a persistent map with robust image features (landmarks) is an important step for implementing the visual simultaneous localization and mapping (SLAM). The image features must have the properties of high repeatability and unique description to be successfully detected at each time step. The research in this paper focuses on the development of algorithms to build...

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“…In the recursive state estimation algorithm, a hand-held binocular vision is utilized as the only sensing device for the measurement. The image depth of extracted features can be obtained using the concept of stereo vision [13]. We treat this camera as a free-moving robot system with unknown inputs.…”

Section: Robot Slammentioning

confidence: 99%

Robot Simultaneous Localization and Mapping Using Speeded-Up Robust Features

Wang

Tung

Feng

2013

AMM

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An algorithm for robot mapping is proposed in this paper using the method of speeded-up robust features (SURF). Since SURFs are scale- and orientation-invariant features, they have higher repeatability than that of the features obtained by other detection methods. Even in the cases of using moving camera, the SURF method can robustly extract the features from image sequences. Therefore, SURFs are suitable to be utilized as the map features in visual simultaneous localization and mapping (SLAM). In this article, the procedures of detection and matching of the SURF method are modified to improve the image processing speed and feature recognition rate. The sparse representation of SURF is also utilized to describe the environmental map in SLAM tasks. The purpose is to reduce the computation complexity in state estimation using extended Kalman filter (EKF). The EKF SLAM with SURF-based map is developed and implemented on a binocular vision system. The integrated system has been successfully validated to fulfill the basic capabilities of SLAM system.

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Robot Visual Simultaneous Localization and Mapping in Dynamic Environments

Cited by 5 publications

References 0 publications

Stereo SLAM in Dynamic Environments Using Semantic Segmentation

Stereo SLAM in Dynamic Environments Using Semantic Segmentation

Robot mapping using local invariant feature detectors

Robot Simultaneous Localization and Mapping Using Speeded-Up Robust Features

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