Obstacle avoidance and path planning for humanoid robots using stereo vision

Sabe, Kohtaro; Fukuchi, Masaki; Gutmann, Jens-Steffen; Ohashi, Takeshi; Kawamoto, Kiyosumi; Yoshigahara, Takayuki

doi:10.1109/robot.2004.1307213

Cited by 156 publications

(82 citation statements)

References 8 publications

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“…Stereo vision [15,16] is a robust approach for obstacle detection, but is limited by the baseline, because when it narrows, this gives rise to noisy estimates. Furthermore, two cameras limit the use of any compact MAVs.…”

Section: Related Workmentioning

confidence: 99%

Obstacle Avoidance Based-Visual Navigation for Micro Aerial Vehicles

2017

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This paper describes an obstacle avoidance system for low-cost Unmanned Aerial Vehicles (UAVs) using vision as the principal source of information through the monocular onboard camera. For detecting obstacles, the proposed system compares the image obtained in real time from the UAV with a database of obstacles that must be avoided. In our proposal, we include the feature point detector Speeded Up Robust Features (SURF) for fast obstacle detection and a control law to avoid them. Furthermore, our research includes a path recovery algorithm. Our method is attractive for compact MAVs in which other sensors will not be implemented. The system was tested in real time on a Micro Aerial Vehicle (MAV), to detect and avoid obstacles in an unknown controlled environment; we compared our approach with related works.

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Section: Related Workmentioning

confidence: 99%

Obstacle Avoidance Based-Visual Navigation for Micro Aerial Vehicles

2017

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“…To obtain 3D information, it calculates the disparities between the matching points of a stereo image pair. The obtained 3D information can be used in a number of applications, such as automotive vehicles, robot vision, monitoring systems, mobile applications, and 3D reconstruction [1][2][3]. Crucial to the reliability of 3D information in these applications is the accurate calculation of disparity that is carried out by two types of stereo matching algorithms: local and global matching algorithms [4,5].…”

Section: Introductionmentioning

confidence: 99%

An Improved Stereo Matching Algorithm with Robustness to Noise Based on Adaptive Support Weight

2017

J Inf Process Syst

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An active research area in computer vision, stereo matching is aimed at obtaining three-dimensional (3D) information from a stereo image pair captured by a stereo camera. To extract accurate 3D information, a number of studies have examined stereo matching algorithms that employ adaptive support weight. Among them, the adaptive census transform (ACT) algorithm has yielded a relatively strong matching capability. The drawbacks of the ACT, however, are that it produces low matching accuracy at the border of an object and is vulnerable to noise. To mitigate these drawbacks, this paper proposes and analyzes the features of an improved stereo matching algorithm that not only enhances matching accuracy but also is also robust to noise. The proposed algorithm, based on the ACT, adopts the truncated absolute difference and the multiple sparse windows method. The experimental results show that compared to the ACT, the proposed algorithm reduces the average error rate of depth maps on Middlebury dataset images by as much as 2% and that is has a strong robustness to noise.

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“…For visual perception, QRIO is equipped with stereo cameras with a field of view of 47´horizontally and 39´vertically, and a FPGA module for stereo processing. This sub system provides disparity images with 12.5 fps in resolutions of 176x144 and 88x72 pixels [17]. We utilize the lower 88x72 pixel resolution in our system which allows to process all frames in real-time on the robot.…”

Section: Methodsmentioning

confidence: 99%

“…The maps are centered on the robot such that they reflect a 4x4 m area around the robot. When the robot moves, the grids are shifted according to odometry information obtained by the kinematic information from one foot to the other [17]. The robot's x s .…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A Floor and Obstacle Height Map for 3D Navigation of a Humanoid Robot

Gutmann¹,

Fukuchi

Fujita

Proceedings of the 2005 IEEE International Conference on Robotics and Automation

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Abstract-With the development of biped robots, systems became able to navigate in a 3 dimensional world, walking up and down stairs, or climbing over small obstacles. We present a method for obtaining a labeled 2.5D grid map of the robot's surroundings. Each cell is marked either as floor or obstacle and contains a value telling the height of the floor or obstacle. Such height maps are useful for path planning and collision avoidance. The method uses a novel combination of a 3D occupancy grid for robust sensor data interpretation and a 2.5D height map for fine resolution floor values. We evaluate our approach using stereo vision on the humanoid robot QRIO and show the advantages over previous methods. Experimental results from navigation runs on an obstacle course demonstrate the ability of the method to generate detailed maps for autonomous navigation.Index Terms-3D perception and navigation, obstacle avoidance, humanoid robot.

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Obstacle avoidance and path planning for humanoid robots using stereo vision

Cited by 156 publications

References 8 publications

Obstacle Avoidance Based-Visual Navigation for Micro Aerial Vehicles

Obstacle Avoidance Based-Visual Navigation for Micro Aerial Vehicles

An Improved Stereo Matching Algorithm with Robustness to Noise Based on Adaptive Support Weight

A Floor and Obstacle Height Map for 3D Navigation of a Humanoid Robot

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