Omnidirectional-vision-based estimation for containment detection of a robotic mower

Yang, Junho; Chung, Soon‐Jo; Hutchinson, Seth; Johnson, David A.; Kise, Michio

doi:10.1109/icra.2015.7140090

Cited by 8 publications

(3 citation statements)

References 28 publications

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“…Other methods such as SeqSLAM [4] exhibit good condition-invariance but fail when viewpoint changes significantly. Many actual and potential robotic scenarios, from military reconnaissance to mowing the lawn [5], [6], involve small cheap robots moving at high speed over variable terrain, and hence revisiting places at different times with significantly different pose orientations. This paper addresses the challenge of robustly recognising these places despite rotation (yaw) change, tilt (roll, pitch) change, and appearance change.…”

Section: Introductionmentioning

confidence: 99%

Skyline-based localisation for aggressively manoeuvring robots using UV sensors and spherical harmonics

Stone

Differt

Milford

et al. 2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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Place recognition is a key capability for navigating robots. While significant advances have been achieved on large, stable platforms such as robot cars, achieving robust performance on rapidly manoeuvring platforms in outdoor natural conditions remains a challenge, with few systems able to deal with both variable conditions and large tilt variations caused by rough terrain. Taking inspiration from biology, we propose a novel combination of sensory modality and image processing to obtain a significant improvement in the robustness of sequence-based image matching for place recognition. We use a UV-sensitive fisheye lens camera to segment sky from ground, providing illumination invariance, and encode the resulting binary images using spherical harmonics to enable rotationinvariant image matching. In combination, these methods also produce substantial pitch and roll invariance, as the spherical harmonics for the sky shape are minimally affected, providing the sky remains visible. We evaluate the performance of our method against a leading appearance-invariant technique (SeqSLAM) and a leading viewpoint-invariant technique (FAB-MAP 2.0) on three new outdoor datasets encompassing variable robot heading, tilt, and lighting conditions in both forested and urban environments. The system demonstrates improved condition-and tilt-invariance, enabling robust place recognition during aggressive zigzag manoeuvring along bumpy trails and at tilt angles of up to 60 degrees.

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Section: Introductionmentioning

confidence: 99%

Skyline-based localisation for aggressively manoeuvring robots using UV sensors and spherical harmonics

Stone

Differt

Milford

et al. 2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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“…In this case, the robot must move on low inclination slopes as well as flat surfaces. Yang et al have been presented a robotic mower with Inertial Measurement Unit (IMU) [ 31 ]. Therefore, in order to control thrust magnitude and direction, research on the use of IMU to measure the slope angle is required.…”

Section: Discussionmentioning

confidence: 99%

Grass Cutting Robot for Inclined Surfaces in Hilly and Mountainous Areas

Nishimura

Yamaguchi

2023

Sensors

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Grass cutting is necessary to prevent grass from diverting essential nutrients and water from crops. Usually, in hilly and mountainous areas, grass cutting is performed on steep slopes with an inclination angle of up to 60° (inclination gradient of 173%). However, such grass cutting tasks are dangerous owing to the unstable positioning of workers. For robots to perform these grass cutting tasks, slipping and falling must be prevented on inclined surfaces. In this study, a robot based on stable propeller control and four-wheel steering was developed to provide stable locomotion during grass cutting tasks. The robot was evaluated in terms of locomotion for different steering methods, straight motion on steep slopes, climbing ability, and coverage area. The results revealed that the robot was capable of navigating uneven terrains with steep slope angles. Moreover, no slipping actions that could have affected the grass cutting operations were observed. We confirmed that the proposed robot is able to cover 99.95% and 98.45% of an area on a rubber and grass slope, respectively. Finally, the robot was tested on different slopes with different angles in hilly and mountainous areas. The developed robot was able to perform the grass cutting task as expected.

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“…The applications for autonomous outdoor robots range from consumer-oriented products like lawn mowers to search-and-rescue robots [1,2]. Advanced sensors, e.g., differential GPS and LIDAR, exist to allow autonomous navigation of robots.…”

Section: Introductionmentioning

confidence: 99%

Cloud-Edge Suppression for Visual Outdoor Navigation

Hoffmann

Möller

2017

Robotics

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Outdoor environments pose multiple challenges for the visual navigation of robots, like changing illumination conditions, seasonal changes, dynamic environments and non-planar terrain. Illumination changes are mostly caused by the movement of the Sun and by changing cloud cover. Moving clouds themselves also are a dynamic aspect of a visual scene. For visual homing algorithms, which compute the direction to a previously visited place by comparing the current view with a snapshot taken at that place, in particular, the changing cloud cover poses a problem, since cloud movements do not correspond to movements of the camera and thus constitute misleading information. We propose an edge-filtering method operating on linearly-transformed RGB channels, which reliably detects edges in the ground region of the image while suppressing edges in the sky region. To fulfill this criterion, the factors for the linear transformation of the RGB channels are optimized systematically concerning this special requirement. Furthermore, we test the proposed linear transformation on an existing visual homing algorithm (MinWarping) and show that the performance of the visual homing method is significantly improved compared to the use of edge-filtering methods on alternative color information.

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Omnidirectional-vision-based estimation for containment detection of a robotic mower

Cited by 8 publications

References 28 publications

Skyline-based localisation for aggressively manoeuvring robots using UV sensors and spherical harmonics

Skyline-based localisation for aggressively manoeuvring robots using UV sensors and spherical harmonics

Grass Cutting Robot for Inclined Surfaces in Hilly and Mountainous Areas

Cloud-Edge Suppression for Visual Outdoor Navigation

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