Humanoid robot localization in complex indoor environments

Hornung, Armin; Wurm, Kai M.; Bennewitz, Maren

doi:10.1109/iros.2010.5649751

Cited by 113 publications

(86 citation statements)

References 23 publications

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“…Hornung et al [10] initially proposed a laser-based localization method for a NAO robot in a miniature 3D world model with extensions to include observation from a monocular camera presented in [11].…”

Section: Related Work a Localization Of Humanoid Robotsmentioning

confidence: 99%

Overlap-based ICP tuning for robust localization of a humanoid robot

Nobili

Scona

Caravagna

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-State estimation techniques for humanoid robots are typically based on proprioceptive sensing and accumulate drift over time. This drift can be corrected using exteroceptive sensors such as laser scanners via a scene registration procedure. For this procedure the common assumption of high point cloud overlap is violated when the scenario and the robot's point-of-view are not static and the sensor's field-of-view (FOV) is limited. In this paper we focus on the localization of a robot with limited FOV in a semi-structured environment. We analyze the effect of overlap variations on registration performance and demonstrate that where overlap varies, outlier filtering needs to be tuned accordingly. We define a novel parameter which gives a measure of this overlap. In this context, we propose a strategy for robust non-incremental registration. The pre-filtering module selects planar macro-features from the input clouds, discarding clutter. Outlier filtering is automatically tuned at run-time to allow registration to a common reference in conditions of non-uniform overlap. An extensive experimental demonstration is presented which characterizes the performance of the algorithm using two humanoids: the NASA Valkyrie, in a laboratory environment, and the Boston Dynamics Atlas, during the DARPA Robotics Challenge Finals.

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Section: Related Work a Localization Of Humanoid Robotsmentioning

confidence: 99%

Overlap-based ICP tuning for robust localization of a humanoid robot

Nobili

Scona

Caravagna

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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“…A humanoid's body frame is usually located in its torso. We first introduce Monte Carlo localization (MCL) based on laser range data to determine the humanoid's full 6D pose in a 3D world model [1].…”

Section: Standard Laser-based 6d MCLmentioning

confidence: 99%

“…Previously, we presented a Monte Carlo localization (MCL) technique that integrates data from a 2D laser scanner, an inertial measurement unit, and joint encoders to estimate a robot's 6D pose in a given 3D model of the environment [1]. This approach yields good results when the robot is walking on flat ground.…”

Section: Introductionmentioning

confidence: 99%

Improved proposals for highly accurate localization using range and vision data

Osswald

Hornung

Bennewitz

2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-In order to successfully climb challenging staircases that consist of many steps and contain difficult parts, humanoid robots need to accurately determine their pose. In this paper, we present an approach that fuses the robot's observations from a 2D laser scanner, a monocular camera, an inertial measurement unit, and joint encoders in order to localize the robot within a given 3D model of the environment. We develop an extension to standard Monte Carlo localization (MCL) that draws particles from an improved proposal distribution to obtain highly accurate pose estimates. Furthermore, we introduce a new observation model based on chamfer matching between edges in camera images and the environment model. We thoroughly evaluate our localization approach and compare it to previous techniques in real-world experiments with a Nao humanoid. The results show that our approach significantly improves the localization accuracy and leads to a considerably more robust robot behavior. Our improved proposal in combination with chamfer matching can be generally applied to improve a range-based pose estimate by a consistent matching of lines obtained from vision.

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“…Otherwise, the robot might bump into the handrail when not being in the center of the steps or slip off the stair edge. For this purpose, our humanoid combines a laser-based localization [21] with observations from its lower camera that covers the area directly in front of its feet. Extracted line segments are matched to the edges of the staircase model to accurately determine the robot's pose on the staircase.…”

Section: Stair Climbingmentioning

confidence: 99%

From 3D point clouds to climbing stairs: A comparison of plane segmentation approaches for humanoids

Osswald

Gutmann²,

Hornung

et al. 2011

2011 11th IEEE-RAS International Conference on Humanoid Robots

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Abstract-In this paper, we consider the problem of building 3D models of complex staircases based on laser range data acquired with a humanoid. These models have to be sufficiently accurate to enable the robot to reliably climb up the staircase. We evaluate two state-of-the-art approaches to plane segmentation for humanoid navigation given 3D range data about the environment. The first approach initially extracts line segments from neighboring 2D scan lines, which are successively combined if they lie on the same plane. The second approach estimates the main directions in the environment by randomly sampling points and applying a clustering technique afterwards to find planes orthogonal to the main directions. We propose extensions for this basic approach to increase the robustness in complex environments which may contain a large number of different planes and clutter. In practical experiments, we thoroughly evaluate all methods using data acquired with a laser-equipped Nao robot in a multi-level environment. As the experimental results show, the reconstructed 3D models can be used to autonomously climb up complex staircases.

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Humanoid robot localization in complex indoor environments

Cited by 113 publications

References 23 publications

Overlap-based ICP tuning for robust localization of a humanoid robot

Overlap-based ICP tuning for robust localization of a humanoid robot

Improved proposals for highly accurate localization using range and vision data

From 3D point clouds to climbing stairs: A comparison of plane segmentation approaches for humanoids

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