Real-time navigation in 3D environments based on depth camera data

Maier, Daniel; Hornung, Armin; Bennewitz, Maren

doi:10.1109/humanoids.2012.6651595

Cited by 85 publications

(50 citation statements)

References 18 publications

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“…The use of range sensors leads to a high accuracy of the estimated pose. We hereby extend our previous work on localization 5,6,7 with a unified approach for range measurements and vision data, compare different sensors and observation models, and improve the performance by means of odometry calibration.…”

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.

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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.

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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.

Self Cite

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“…To finish the current section, it is worth noting that assessing localization without considering feedback has been a common practice in the literature Maier et al, 2012;Obwald et al, 2012;Alcantarilla et al, 2013;Hornung et al, 2014). Specifically, a previously computed path is performed by the robot, where localization is not an active part for achieving the desired path.…”

Section: Persistent Localizationmentioning

confidence: 99%

“…Unfortunately, as a humanoid robot march implies constant swinging, the risk of sudden changes in the motion of the camera may compromise the applicability of these approaches. Still, Maier et al (2012) proposed an integrated navigation framework that considers localization, obstacle mapping and collision avoidance using an RGB-D camera mounted on top of the head of a NAO robot. To this end, an internal map is represented through an octree , while the pose of the robot is estimated using Monte Carlo localization based on depth information.…”

Section: 3mentioning

confidence: 99%

Vision based persistent localization of a humanoid robot for locomotion tasks

Castelán

Arechavaleta

2016

International Journal of Applied Mathematics and Computer Science

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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.

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“…The topological system is designed to work with multiple exteroceptive 25,26 and proprioceptive events, handling them simultaneously through a perception interface and an event manager. This innovative concept is based on human perception when navigating, rotating the system with a multi-sensorial navigation strategy.…”

Section: Perception Interface and Event Managermentioning

confidence: 99%

A topological navigation system for indoor environments based on perception events

Gómez

Hernandez

Crespo

et al. 2016

International Journal of Advanced Robotic Systems

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The aim of the work presented in this article is to develop a navigation system that allows a mobile robot to move autonomously in an indoor environment using perceptions of multiple events. A topological navigation system based on events that imitates human navigation using sensorimotor abilities and sensorial events is presented. The increasing interest in building autonomous mobile systems makes the detection and recognition of perceptions a crucial task. The system proposed can be considered a perceptive navigation system as the navigation process is based on perception and recognition of natural and artificial landmarks, among others. The innovation of this work resides in the use of an integration interface to handle multiple events concurrently, leading to a more complete and advanced navigation system. The developed architecture enhances the integration of new elements due to its modularity and the decoupling between modules. Finally, experiments have been carried out in several mobile robots, and their results show the feasibility of the navigation system proposed and the effectiveness of the sensorial data integration managed as events.

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Real-time navigation in 3D environments based on depth camera data

Cited by 85 publications

References 18 publications

Monte Carlo Localization for Humanoid Robot Navigation in Complex Indoor Environments

Monte Carlo Localization for Humanoid Robot Navigation in Complex Indoor Environments

Vision based persistent localization of a humanoid robot for locomotion tasks

A topological navigation system for indoor environments based on perception events

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