Indoor Navigation for a Humanoid Robot Using a View Sequence

Ido, Junichi; Shimizu, Yoshinao; Matsumoto, Yoshio; Ogasawara, Tsukasa

doi:10.1177/0278364908095841

Cited by 66 publications

(31 citation statements)

References 278 publications

(421 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An important aspect of the method is the use of the pressure sensors attached to the feet to activate the other sensor readings. This is consistent with the experiments presented by Ido et al (2009) to overcome the acquisition of blurred images caused by the impacts between the feet and the ground. The undesired effects produced by the lateral movements are studied by Oriolo et al (2013).…”

Section: 1supporting

confidence: 92%

“…This requirement translates to an active localization formulation where, at each step, the next control input for a given time horizon should consider visibility criteria to direct the humanoid walking. In this sense, an active topological localization strategy has been proposed by Ido et al (2009) to compute the next action at a given time horizon based on a sequence of reference images. Recently, a similar strategy, suggested by Delfin et al (2014), considers predefined reference images to guide the humanoid walking towards a target image by applying a sequence of continuous visual servo control laws.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Vision based persistent localization of a humanoid robot for locomotion tasks

Castelán

Arechavaleta

2016

International Journal of Applied Mathematics and Computer Science

View full text Add to dashboard Cite

Typical monocular localization schemes involve a search for matches between reprojected 3D world points and 2D image features in order to estimate the absolute scale transformation between the camera and the world. Successfully calculating such transformation implies the existence of a good number of 3D points uniformly distributed as reprojected pixels around the image plane. This paper presents a method to control the march of a humanoid robot towards directions that are favorable for visual based localization. To this end, orthogonal diagonalization is performed on the covariance matrices of both sets of 3D world points and their 2D image reprojections. Experiments with the NAO humanoid platform show that our method provides persistence of localization, as the robot tends to walk towards directions that are desirable for successful localization. Additional tests demonstrate how the proposed approach can be incorporated into a control scheme that considers reaching a target position.

show abstract

Section: 1supporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Vision based persistent localization of a humanoid robot for locomotion tasks

Castelán

Arechavaleta

2016

International Journal of Applied Mathematics and Computer Science

View full text Add to dashboard Cite

show abstract

“…For example, Ido et al 8 apply a visionbased approach and compare the current image to previously recorded reference images in order to estimate the location of the robot. Oßwald et al 9 and Bennewitz et al 10 compare visual features to a previously learned 2D feature map during pose tracking.…”

Section: Related Workmentioning

confidence: 99%

Monte Carlo Localization for Humanoid Robot Navigation in Complex Indoor Environments

Hornung

Oßwald

Maier

et al. 2014

Int. J. Human. Robot.

View full text Add to dashboard Cite

Accurate and reliable localization is a prerequisite for autonomously performing highlevel tasks with humanoid robots. In this article, we present a probabilistic localization method for humanoid robots navigating in arbitrary complex indoor environments using only onboard sensing, which is a challenging task. Inaccurate motion execution of biped robots leads to an uncertain estimate of odometry, and their limited payload constrains perception to observations from lightweight and typically noisy sensors. Additionally, humanoids do not walk on flat ground only and perform a swaying motion while walking, which requires estimating a full 6D torso pose. We apply Monte Carlo localization to globally determine and track a humanoid's 6D pose in a given 3D world model, which may contain multiple levels and staircases. We present an observation model to integrate range measurements from a laser scanner or a depth camera as well as attitude data and information from the joint encoders. To increase the localization accuracy, e.g., while climbing stairs, we propose a further observation model and additionally use monocular vision data in an improved proposal distribution. We demonstrate the effectiveness of our methods in extensive real-world experiments with a Nao humanoid. As the experiments illustrate, the robot is able to globally localize itself and accurately track its 6D pose while walking and climbing stairs.

show abstract

“…For example, Ido et al [6] apply a visionbased approach and compare the current image to previously recorded reference images in order to estimate the location of the robot. Oßwald et al [7] and Bennewitz et al [8] compare visual features to a previously learned 2D feature map during pose tracking.…”

Section: Related Workmentioning

confidence: 99%

Humanoid robot localization in complex indoor environments

Hornung

Wurm

Bennewitz

2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

114

View full text Add to dashboard Cite

Abstract-In this paper, we present a localization method for humanoid robots navigating in arbitrary complex indoor environments using only onboard sensing. Reliable and accurate localization for humanoid robots operating in such environments is a challenging task. First, humanoids typically execute motion commands rather inaccurately and odometry can be estimated only very roughly. Second, the observations of the small and lightweight sensors of most humanoids are seriously affected by noise. Third, since most humanoids walk with a swaying motion and can freely move in the environment, e.g., they are not forced to walk on flat ground only, a 6D torso pose has to be estimated. We apply Monte Carlo localization to globally determine and track a humanoid's 6D pose in a 3D world model, which may contain multiple levels connected by staircases. To achieve a robust localization while walking and climbing stairs, we integrate 2D laser range measurements as well as attitude data and information from the joint encoders. We present simulated as well as real-world experiments with our humanoid and thoroughly evaluate our approach. As the experiments illustrate, the robot is able to globally localize itself and accurately track its 6D pose over time.

show abstract

Indoor Navigation for a Humanoid Robot Using a View Sequence

Cited by 66 publications

References 278 publications

Vision based persistent localization of a humanoid robot for locomotion tasks

Vision based persistent localization of a humanoid robot for locomotion tasks

Monte Carlo Localization for Humanoid Robot Navigation in Complex Indoor Environments

Humanoid robot localization in complex indoor environments

Contact Info

Product

Resources

About