Self-calibration for a 3D laser

Sheehan, Mark; Harrison, Alastair; Newman, Paul

doi:10.1177/0278364911429475

Cited by 80 publications

(81 citation statements)

References 26 publications

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“…Second, we build models of tracked objects by aligning the point clouds based on our estimated velocity. We then compute a crispness score, as in Sheehan et al [23], to compare how correctly the models were constructed. We use this method to evaluate our tracking accuracy on a large number of people, bikes, and moving cars.…”

Section: Resultsmentioning

confidence: 99%

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Combining 3D Shape, Color, and Motion for Robust Anytime Tracking

Held

Levinson

Thrun

et al. 2014

Robotics: Science and Systems X

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Abstract-Although object tracking has been studied for decades, real-time tracking algorithms often suffer from low accuracy and poor robustness when confronted with difficult, realworld data. We present a tracker that combines 3D shape, color (when available), and motion cues to accurately track moving objects in real-time. Our tracker allocates computational effort based on the shape of the posterior distribution. Starting with a coarse approximation to the posterior, the tracker successively refines this distribution, increasing in tracking accuracy over time. The tracker can thus be run for any amount of time, after which the current approximation to the posterior is returned. Even at a minimum runtime of 0.7 milliseconds, our method outperforms all of the baseline methods of similar speed by at least 10%. If our tracker is allowed to run for longer, the accuracy continues to improve, and it continues to outperform all baseline methods. Our tracker is thus anytime, allowing the speed or accuracy to be optimized based on the needs of the application. I. INTRODUCTIONMany robotics applications are limited in what they can achieve due to unreliable tracking estimates. For example, an autonomous vehicle driving past a row of parked cars should know if one of these cars is about to pull out into the lane. Current state-of-the-art trackers give noisy estimates of the velocity of these vehicles, which are difficult to track due to heavy occlusion and viewpoint changes. Additionally, without robust estimates of the velocity of nearby vehicles, merging onto or off of highways or changing lanes become formidable tasks. Similar issues will be encountered by any robot that must act autonomously in crowded, dynamic environments.Our tracker makes use of the full 3D shape of the object being tracked, which allows us to robustly track objects despite occlusions or changes in viewpoint. We place the 3D shape information in a probabilistic framework, in which we combine cues from shape, color, and motion. As we will show, adding color and motion information gives a large benefit to our system compared to using the 3D shape alone. This information is especially useful for distant objects or objects under heavy occlusions, when detailed 3D shape information may not be available.We make use of a grid-based method to sample velocities from the state space. Traditional grid-based approaches are too slow to track multiple objects in real-time. We are able to finely sample from a large grid in real-time through the use of a novel method called annealed dynamic histograms. We start by sampling from the state space at a coarse resolution, using an approximation to the posterior distribution over velocities. As the sampling resolution increases, we anneal this distribution,

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Section: Resultsmentioning

confidence: 99%

“…These models can be visualized in Figures 1 and 8. For each model, we compute a crispness score [23] to evaluate how correctly the models were constructed; an accurate model corresponds to an accurately tracked object. As can be seen in Figure 9, if the tracking is not accurate, the resulting model will be very noisy.…”

Section: Evaluation: Model Crispnessmentioning

confidence: 99%

Combining 3D Shape, Color, and Motion for Robust Anytime Tracking

Held

Levinson

Thrun

et al. 2014

Robotics: Science and Systems X

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“…If a laser rotates while the robot is moving, an IMU (or other device) can be used to track the laser's position over time, which allows laser points to be projected into the camera image based on the laser's position at time the image was captured. In such a case, it is helpful to first calibrate the location of laser itself [9,11,8,21]; we use the procedure described in [8], which also works automatically and in arbitrary scenes.…”

Section: Sensor Processingmentioning

confidence: 99%

Automatic Online Calibration of Cameras and Lasers

Levinson

Thrun

2013

Robotics: Science and Systems IX

257

158

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Abstract-The combined use of 3D scanning lasers with 2D cameras has become increasingly popular in mobile robotics, as the sparse depth measurements of the former augment the dense color information of the latter. Sensor fusion requires precise 6-DOF transforms between the sensors, but hand-measuring these values is tedious and inaccurate. In addition, autonomous robots can be rendered inoperable if their sensors' calibrations change over time. Yet previously published camera-laser calibration algorithms are offline only, requiring significant amounts of data and/or specific calibration targets; they are thus unable to correct calibration errors that occur during live operation.In this paper, we introduce two new real-time techniques that enable camera-laser calibration online, automatically, and in arbitrary environments. The first is a probabilistic monitoring algorithm that can detect a sudden miscalibration in a fraction of a second. The second is a continuous calibration optimizer that adjusts transform offsets in real time, tracking gradual sensor drift as it occurs.Although the calibration objective function is not globally convex and cannot be optimized in real time, in practice it is always locally convex around the global optimum, and almost everywhere else. Thus, the local shape of the objective function at the current parameters can be used to determine whether the sensors are calibrated, and allows the parameters to be adjusted gradually so as to maintain the global optimum.In several online experiments on thousands of frames in real markerless scenes, our method automatically detects miscalibrations within one second of the error exceeding .25 deg or 10cm, with an accuracy of 100%. In addition, rotational sensor drift can be tracked in real-time with a mean error of just .10 deg. Together, these techniques allow significantly greater flexibility and adaptability of robots in unknown and potentially harsh environments.

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“…In the airborne laser scanning community, automatic calibration approaches are known (Skaloud and Schaer, 2007), and similarly vehicle-based kinematic laser scanning has been considered (Rieger et al, 2010). In the robotics community there exist approaches for calibrating several range scanners semi-automatically, i.e., with manually labeled data (Underwood et al, 2009) or using automatically computed quality metrics (Sheehan et al, 2011;Elseberg et al, 2013). Often vendors do not make their calibration methods public and unfortunately, the authors of this paper have no information on the calibration of the Google Cartographer backpack.…”

Section: Calibration Referencing and Slammentioning

confidence: 99%

Improving Google's Cartographer 3d Mapping by Continuous-Time Slam

Nüchter

Bleier

Schauer

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper shows how to use the result of Google's SLAM solution, called Cartographer, to bootstrap our continuous-time SLAM algorithm. The presented approach optimizes the consistency of the global point cloud, and thus improves on Google's results. We use the algorithms and data from Google as input for our continuous-time SLAM software. We also successfully applied our software to a similar backpack system which delivers consistent 3D point clouds even in absence of an IMU.

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Self-calibration for a 3D laser

Cited by 80 publications

References 26 publications

Combining 3D Shape, Color, and Motion for Robust Anytime Tracking

Combining 3D Shape, Color, and Motion for Robust Anytime Tracking

Automatic Online Calibration of Cameras and Lasers

Improving Google's Cartographer 3d Mapping by Continuous-Time Slam

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