Relative localization using path odometry information

Doh, Nakju Lett; Choset, Howie; Chung, Wan Kyun

doi:10.1007/s10514-006-6474-8

Cited by 40 publications

(24 citation statements)

References 15 publications

(19 reference statements)

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“…Careful calibration may work for short distances but will eventually require corrections [3], [4]. Particle filters have been used to correct drift errors by using walls of a known map to constrain particles to a given free space with odometry motion data [2], [5], [11], [14], [17].…”

Section: Related Workmentioning

confidence: 99%

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O-Snap: Optimal snapping of odometry trajectories for route identification

Wang

Veloso

Seshan

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-An increasing number of wearable and mobile devices are capable of automatically sensing and recording rich information about the surrounding environment. To make use of such data, it is desirable for each data point to be matched with its corresponding spatial location. We focus on using the trajectory from a device's odometry sensors that reveal changes in motion over time. Our goal is to recover the route traversed, which we will define as a sequence of revisitable positions. Dead reckoning, which computes the device's route from its odometry trajectory, is known to suffer from significant drift over time. We aim to overcome drift errors by reshaping the odometry trajectory to fit the constraints of a given topological map and sensor noise model. Prior works use iterative search algorithms that are susceptible to local maximas [15], which means that they can be misled when faced with ambiguous decisions. In contrast, our algorithm is able to find the set of all routes within the given constraints. This also reveals if there are multiple routes that are similarly likely. We can then rank them and select the optimal route that is most likely to be the actual route. We also show that the algorithm can be extended to recover routes even in the presence of topological map errors. We evaluate our algorithm by recovering all routes traversed by a wheeled robot covering over 9 kilometers from its odometry sensor data.

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Section: Related Workmentioning

confidence: 99%

“…It has been used for both topological localization with visual features [12] for robots as well as GPS map matching algorithms [3], [6], [8], [9], [16]. The major difference is that visual features and GPS positions both capture unique signatures in an environment while our algorithm operates only on featureless, relative motion data.…”

Section: Related Workmentioning

confidence: 99%

O-Snap: Optimal snapping of odometry trajectories for route identification

Wang

Veloso

Seshan

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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“…More recently, a method based on two successive least-squares estimations has been introduced [2]. Finally, very few approaches calibrate the odometry without the need of an a priori knowledge of the environment and/or of the use of global position sensors (like a GPS) [6], [17], [20].…”

Section: A Previous Workmentioning

confidence: 99%

Using the distribution theory to simultaneously calibrate the sensors of a mobile robot

Martinelli¹

2009

Robotics: Science and Systems V

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Abstract-This paper introduces a simple and very efficient strategy to extrinsically calibrate a bearing sensor (e.g. a camera) mounted on a mobile robot and simultaneously estimate the parameters describing the systematic error of the robot odometry system. The paper provides two contributions. The first one is the analytical computation to derive the part of the system which is observable when the robot accomplishes circular trejectories. This computation consists in performing a local decomposition of the system, based on the theory of distributions. In this respect, this paper represents the first application of the distribution theory in the frame-work of mobile robotics. Then, starting from this decomposition, a method to efficiently estimate the parameters describing both the extrinsic bearing sensor calibration and the odometry calibration is derived (second contribution). Simulations and experiments with the robot e-Puck equipped with encoder sensors and a camera validate the approach.

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“…While one can localize over short distances [2], odometry will eventually succumb to dead reckoning errors and require corrections to fix these cumulative errors.…”

Section: Introductionmentioning

confidence: 99%

Iterative Snapping of Odometry Trajectories for Path Identification

Wang

Veloso

Seshan

2014

RoboCup 2013: Robot World Cup XVII

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Abstract. An increasing number of mobile devices are capable of automatically sensing and recording rich information about the surrounding environment. Spatial locations of such data can help to better learn about the environment. In this work, we address the problem of identifying the locations visited by a mobile device as it moves within an indoor environment. We focus on devices equipped with odometry sensors that capture changes in motion. Odometry suffers from cumulative errors of dead reckoning but it captures the relative shape of the traversed path well. Our approach will correct such errors by matching the shape of the trajectory from odometry to traversable paths of a known map. Our algorithm is inspired by prior vehicular GPS map matching techniques that snap global GPS measurements to known roads. We similarly wish to snap the trajectory from odometry to known hallways. Several modifications are required to ensure these techniques are robust when given relative measurements from odometry. If we assume an office-like environment with only straight hallways, then a significant rotation indicates a transition to another hallway. As a result, we partition the trajectory into line segments based on significant turns. Each trajectory segment is snapped to a corresponding hallway that best maintains the shape of the original trajectory. These snapping decisions are made based on the similarity of the two curves as well as the rotation to transition between hallways. We will show robustness under different types of noise in complex environments and the ability to propose coarse sensor noise errors.

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Relative localization using path odometry information

Cited by 40 publications

References 15 publications

O-Snap: Optimal snapping of odometry trajectories for route identification

O-Snap: Optimal snapping of odometry trajectories for route identification

Using the distribution theory to simultaneously calibrate the sensors of a mobile robot

Iterative Snapping of Odometry Trajectories for Path Identification

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