Efficient on-board Stereo SLAM through constrained-covisibility strategies

Castro, Gaston; Nitsche, Matıas; Pire, Taihú; Fischer, Thomas; Cristóforis, Pablo De

doi:10.1016/j.robot.2019.03.015

Cited by 9 publications

(3 citation statements)

References 13 publications

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“…Hauke Strasdat's [19] research showed that, through progress in computer processing power and algorithms, keyframe-based bundle adjustment outperformed filtering. A plethora of excellent batch nonlinear optimization methods have emerged [20,21]. A keyframe-based sliding window strategy was used to build a metric constraint around the current frame for jointly optimizing keyframes and landmarks [22], after balancing accuracy and speed.…”

Section: Related Workmentioning

confidence: 99%

Stereovision-Based Ego-Motion Estimation for Combine Harvesters

Chen

Guan

et al. 2022

Sensors

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Ego-motion estimation is a foundational capability for autonomous combine harvesters, supporting high-level functions such as navigation and harvesting. This paper presents a novel approach for estimating the motion of a combine harvester from a sequence of stereo images. The proposed method starts with tracking a set of 3D landmarks which are triangulated from stereo-matched features. Six Degree of Freedom (DoF) ego motion is obtained by minimizing the reprojection error of those landmarks on the current frame. Then, local bundle adjustment is performed to refine structure (i.e., landmark positions) and motion (i.e., keyframe poses) jointly in a sliding window. Both processes are encapsulated into a two-threaded architecture to achieve real-time performance. Our method utilizes a stereo camera, which enables estimation at true scale and easy startup of the system. Quantitative tests were performed on real agricultural scene data, comprising several different working paths, in terms of estimating accuracy and real-time performance. The experimental results demonstrated that our proposed perception system achieved favorable accuracy, outputting the pose at 10 Hz, which is sufficient for online ego-motion estimation for combine harvesters.

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Section: Related Workmentioning

confidence: 99%

Stereovision-Based Ego-Motion Estimation for Combine Harvesters

Chen

Guan

et al. 2022

Sensors

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“…This paper describes the development and research status of visual SLAM and adopts a semi-direct method visual SLAM system for dynamic scenes to cope with problems such as dynamic object interference in complex environments by combining the feature point method and the direct method in response to the poor performance of visual SLAM systems for dynamic environments today [6]. The so-called semi-direct method refers to the acquisition of camera bit pose by direct matching of feature point image blocks in the image, instead of using direct matching for the whole image (filtering the points to be matched according to the gray gradient size) as in the direct matching method [7].…”

Section: Introductionmentioning

confidence: 99%

Vision-Based SLAM for Uavs in Dynamic Environments

Huang

2023

HSET

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Visual SLAM (Simultaneous Localization And Mapping) is a technique mainly used for robot navigation and positioning. It uses vision sensors to map the environment and estimate its own location. In dynamic environment, they are inevitably affected by dynamic objects, which leads to the decrease of accuracy and stability of the system. In this paper, the visual odometer in SLAM system is studied, and the semi-direct visual odometer (SVO) algorithm is improved on the basis of ORB-SLAM framework. The basic principle of the algorithm is to treat pixel matching between adjacent image frames as an optimization problem, and use direct registration method to obtain camera pose for sparse feature blocks, avoiding a lot of feature extraction and matching. When a key frame appears, ORB (Oriented FAST and Rotated BRIEF) features of the image are extracted, and feature matching is used to track local maps to obtain more mapping relationships. Moreover, camera pose and three-dimensional structure of the scene are estimated by minimizing reprojection errors, thus improving positioning accuracy and map construction quality. Finally, experiments on several public data sets show that the improved semi-direct visual odometer scheme in this paper still has good robustness to moving objects in dynamic scenes. While maintaining high accuracy, it obtains better real-time performance by tracking visual feature points.

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“…На сегодняшний день хорошо развиты два подхода к решению этой задачи: V-SLAM [1,2] и визуальная одометрия [3,4]. Современные версии упомянутых алгоритмов показывают хорошие результаты и могут работать в реальном времени [5,6].…”

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Solution of problem of returning to the starting point of autonomously flying UAV by visual navigation

Жук

Залесский

Троцкий

2020

Informatika (Minsk)

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An autonomous visual navigation algorithm is considered, designed for “home“ return of unmanned aerial vehicle (UAV) equipped with on-board video camera and on-board computer, out of GPS and GLONASS navigation signals. The proposed algorithm is similar to the well-known visual navigation algorithms such as V-SLAM (simultaneous localization and mapping) and visual odometry, however, it differs in separate implementation of mapping and localization processes. It calculates the geographical coordinates of the features on the frames taken by on-board video camera during the flight from the start point until the moment of GPS and GLONASS signals loss. After the loss of the signal the return mission is launched, which provides estimation of the position of UAV relatively the map created by previously found features. Proposed approach does not require such complex calculations as V-SLAM and does not accumulate errors over time, in contrast to visual odometry and traditional methods of inertial navigation. The algorithm was implemented and tested with use of DJI Phantom 3 Pro quadcopter.

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Efficient on-board Stereo SLAM through constrained-covisibility strategies

Cited by 9 publications

References 13 publications

Stereovision-Based Ego-Motion Estimation for Combine Harvesters

Stereovision-Based Ego-Motion Estimation for Combine Harvesters

Vision-Based SLAM for Uavs in Dynamic Environments

Solution of problem of returning to the starting point of autonomously flying UAV by visual navigation

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