Visual Teach and Repeat using appearance-based lidar

McManus, Colin; Furgale, Paul; Stenning, Braden; Barfoot, Timothy D.

doi:10.1109/icra.2012.6224654

Cited by 35 publications

(24 citation statements)

References 26 publications

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“…The navigation system is based on visual teach and repeat (T&R) [18], [19], which is often employed in the space domain and similar approach can used for example in sample and return missions [20], [21]. T&R enables a mobile robot to autonomously follow a previously driven path with high accuracy.…”

Section: A Systemmentioning

confidence: 99%

Scheduled perception for energy-efficient path following

Ondrúška

Gurău

Marchegiani

et al. 2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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This paper explores the idea of reducing a robot's energy consumption while following a trajectory by turning off the main localisation subsystem and switching to a lowerpowered, less accurate odometry source at appropriate times. This applies to scenarios where the robot is permitted to deviate from the original trajectory, which allows for energy savings. Sensor scheduling is formulated as a probabilistic belief planning problem. Two algorithms are presented which generate feasible perception schedules: the first is based upon a simple heuristic; the second leverages dynamic programming to obtain optimal plans. Both simulations and real-world experiments on a planetary rover prototype demonstrate over 50% savings in perception-related energy, which translates into a 12% reduction in total energy consumption. I. INTRODUCTIONRobots require energy to operate. Yet they only have access to limited energy storage during missions. As we extend the reach of autonomous systems to operate in remote locations, over long distances and for long periods of time, energy considerations are becoming increasingly important. To date, these considerations are often brought to bear in schemes where trajectories or speed profiles are optimised to minimise the energy required for actuation (see, for example, [1], [2], [3]). Here we take a different, yet complementary, approach in considering the energy expenditure for sensing (and, implicitly, computation) associated with navigation. In particular, our goal is to activate the perception system only as required to maintain the vehicle within a given margin around a predetermined path. As the main navigation sensors are switched off and the robot reverts to a lower-powered, less accurate odometry source for parts of the trajectory, any associated computation will also be reduced, leading to further savings in energy.Naively, such perception schedules could be constructed by switching sensors on and off randomly or according to, for example, a fixed frequency. This does, however, suffer the drawback that no heed is paid to drift in the robot's position with respect to the original trajectory: it may not be desirable to deviate by more than an allowed margin from the predetermined route. This arises, for example, in a planetary exploration scenario when conducting long traverses over featureless terrain. Other possible considerations include traversability, obstacles, and the robustness of the localisation system to deviations from the original path. Such naive approaches would also need to be tuned to individual trajectories as savings would depend significantly on trajectory shape. In this work we present two approaches which explicitly account for drift and trajectory shape (though the

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Section: A Systemmentioning

confidence: 99%

Scheduled perception for energy-efficient path following

Ondrúška

Gurău

Marchegiani

et al. 2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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“…Figure 6 provides a overview of our field robot hardware configuration. Details of the hardware configuration and the original field experiment can be found in [18]. Figure 7 shows that without calibration the rover pose estimate suffered from a slow drift in pitch.…”

Section: Driving Resultsmentioning

confidence: 99%

Two-axis scanning lidar geometric calibration using intensity imagery and distortion mapping

Dong

Anderson

Barfoot

2013

2013 IEEE International Conference on Robotics and Automation

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Accurate pose estimation relies on high-quality sensor measurements. Due to manufacturing tolerance, every sensor (camera or lidar) needs to be individually calibrated. Feature-based techniques using simple calibration targets (e.g., a checkerboard pattern) have become the dominant approach to camera sensor calibration. Existing lidar calibration methods require a controlled environment (e.g., a space of known dimension) or specific configurations of supporting hardware (e.g., coupled with GPS/IMU). Leveraging recent state estimation developments based on lidar intensity imagery, this paper presents a calibration procedure for a two-axis scanning lidar using only an inexpensive checkerboard calibration target. In addition, the proposed method generalizes a two-axis scanning lidar as an idealized spherical camera with additive measurement distortions. Conceptually, this is not unlike normal camera calibration in which an arbitrary camera is modelled as an idealized projective (pinhole) camera with tangential and radial distortions. The resulting calibration method, we believe, can be readily applied to a variety of two-axis scanning lidars. We present the measurement improvement quantitatively, as well as the impact of calibration on a 1.1-km visual odometry estimate.

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“…Once we have associated these scene signatures, we can perform local, metric, pose estimation. This approach of predicting where the nearest topological node is and then localising against the map is similar to teach-and-repeat systems such as McManus et al [23] and Furgale and Barfoot [1], except that our map keyframes are separated by larger distances. Figure 6 presents the localisation results for the 5 live runs against our map that contains a bank of trained classifiers per place.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Scene Signatures: Localised and Point-less Features for Localisation

McManus

Upcroft

Newmann³

2014

Robotics: Science and Systems X

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116

104

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Abstract-This paper is about localising across extreme lighting and weather conditions. We depart from the traditional point-feature-based approach since matching under dramatic appearance changes is a brittle and hard. Point-feature detectors are rigid procedures which pass over an image examining small, low-level structure such as corners or blobs. They apply the same criteria to all images of all places. This paper takes a contrary view and asks what is possible if instead we learn a bespoke detector for every place. Our localisation task then turns into curating a large bank of spatially indexed detectors and we show that this yields vastly superior performance in terms of robustness in exchange for a reduced but tolerable metric precision. We present an unsupervised system that produces broad-region detectors for distinctive visual elements, called scene signatures, which can be associated across almost all appearance changes. We show, using 21 km of data collected over a period of 3 months, that our system is capable of producing metric estimates from night-to-day or summer-to-winter conditions.

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Visual Teach and Repeat using appearance-based lidar

Cited by 35 publications

References 26 publications

Scheduled perception for energy-efficient path following

Scheduled perception for energy-efficient path following

Two-axis scanning lidar geometric calibration using intensity imagery and distortion mapping

Scene Signatures: Localised and Point-less Features for Localisation

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