An Adaptive Structure Filter for Sensor Registration from Unstructured Terrain

Phillips, Tyson Govan; Green, M. E.; McĂree, P. R.

doi:10.1002/rob.21562

Cited by 10 publications

(18 citation statements)

References 24 publications

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“…The Velodyne HDL-64E has a sensor measurement uncertainty (1σ sensor ) of 20 mm (Velodyne LiDAR Inc, 2008), hence v k ∼ N (0, σ 2 sensor ). A previous study in registering this sensor to mining platforms found that the registration parameters could be recovered with 1σ uncertainties of approximately 10 mm and 1 mrad in position and orientation respectively (Phillips et al, 2014). The deviation to sensor pose is drawn from this parameter covariance, i.e.…”

Section: Assessing Univariate Measurement Likelihoodmentioning

confidence: 99%

“…Scans typically have in the order of 20-30 thousand useful range measurements. The sensor is registered to the machine house using methods 2 real time kinematic global navigation satellite system/inertial measurement unit solution 130 5.3 Five challenges in applying iterative closest point for truck and dipper pose estimation described in Phillips et al (2014). Typical data from a scan is shown in Fig.…”

Section: The Problemmentioning

confidence: 99%

“…Phillips et al (2014) estimates the standard deviation for the translational components of T H→S to be 0.01 m and orientation components to be 10 mrad. The conditional range likelihood,f (z i | H j ), is the summation of kernels K( · ), located at each ray-cast,ẑ i|j,k ,f…”

Section: Univariate Measurement Likelihood Under a Hypothesismentioning

confidence: 99%

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Determining and verifying object pose from LiDAR measurements to support the perception needs of an autonomous excavator

Phillips¹

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This thesis is a study of how light detection and ranging, also known as LiDAR, can be used to meet perception requirements for field robot applications.LiDAR works by having an emitter produce a pulse of light along a known spatial direction. The distance or range to the world along that direction is determined by measuring the time it takes for reflections of the emitted light to be received at a collector. When this is done over multiple spatial directions, a cloud of points is produced that represent a sampling of the world. LiDAR is a mature technology and commercially available sensors are capable of producing point-clouds comprising up to 65,000 points over 360 degree fields of view, that are updated every 50 ms.This thesis is about how an autonomous machine can interpret meaning from these point-clouds in situations where the environment is complex and dynamic, where there is interaction with other agents leading to occlusion and where there is significant dust in the atmosphere.These conditions are systemic to open-cut mining environments, the domain where this thesis draws its motivation. In particular, the thesis uses as its primary example, the perception requirements for automation of a class of large mining excavators known as electric mining shovels. The work is specifically concerned with the problems of using point-clouds from machine mounted sensors to inform and verify knowledge of object position and orientation in the surrounds of these machines, for instance, the trucks being loaded.LiDAR is affected by dust and some see this a fatal flaw for mining robotics. The work starts with an exploration of the behaviour of a commercial LiDAR sensor in the presence of dust and finds that measurements are systematic and predictable. LiDAR sensors are found to exhibit four behaviours and it is shown how these behaviours can be understood from physics-based arguments. Several conclusions emerge, most notably where LiDAR measures dust, it does so to the leading edge of a dust-cloud rather than, say, as a random point within the cloud. This provides powerful insights into how better to use measurements from LiDAR when dust is present, and in particular suggests that dust can be treated like other forms of occlusion.A problem that must be solved to use LiDAR for machine perception is to establish from where each sensor views the world. This is sometimes called sensor registration and it amounts to finding where a sensor is positioned and how it is orientated. This is typically done by placing markers at known locations and computing the sensor registration from where they appear in the sensor data. For mining robot applications, the use of standard marker or artificial-feature-based approaches is infeasible. The thesis develops a method for sensor registration that overcomes this concern by utilizing the geometric structure of the terrain surrounding the autonomous vehicle platform. The method determines the i information content of registration parameters in measurements of the terrain, and update...

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Section: Assessing Univariate Measurement Likelihoodmentioning

confidence: 99%

Section: The Problemmentioning

confidence: 99%

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Determining and verifying object pose from LiDAR measurements to support the perception needs of an autonomous excavator

Phillips¹

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“…The Velodyne LiDAR is registered to the navigation solution using the method described in Phillips et al (2014) and has been assessed as being precise to 0.01 m and 0.05…”

Section: Measurement Datamentioning

confidence: 99%

“…Surfaces fitted to the quasi point clouds are matched using a generalized GaussMarkov model, minimizing the sum of squared Euclidean distances between surfaces. The approach is claimed to improve robustness although it suffers in two respects: (i) range errors associated with high intensity returns tend to be larger than those low intensity returns (Phillips et al, 2014); and (ii) intensity measurements are subject to variation, depending for instance, on the angle of incidence of the LiDAR ray to the surface. Hebel and Stilla (2007) use intensity information to constrain the correspondence estimation.…”

Section: Features In Point Cloud Datamentioning

confidence: 99%

Scan matching for terrain mapping in open-pit mining

Aguirre¹,

Antonio²

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This thesis is concerned with the problem of creating three-dimensional terrain maps using a highdensity, high-rate LiDAR sensor mounted on a moving platform without the use of an external localization solution. In particular, the thesis is motivated by the problem of building terrain maps in open-pit mining environments from data produced by haul-truck mounted LiDAR sensors.A foundation problem in map building from such data is the process of assembling together the point clouds generated by individual sensor scans into a common frame of reference. This falls into a broader class of well-studied problems called scan matching. Scan matching is most commonly solved by the Iterative Closet Point (ICP) method.This thesis makes two contributions. The first contribution is a comparison of the different ICP variants for the candidate application. The literature is replete with different algorithms that can be used at different stages of the ICP process. A population of 20,736 ICP-variants drawn from methods proposed in the literature is compared. Whilst others have looked to compare different ICP-variants, this investigation is distinguished from these earlier works in three respects: i) its comprehensiveness; ii) the focus of its target application (the loosely structured terrain of open-cut mining); and iii) its emphasis on how accuracy, precision, and computational efficiency trade-off across different variants. The main finding of this investigation is that the geometry of the point cloud critically determines the quality of the scan match. Significantly, of the variants considered, none were found to simultaneous meet requirements on accuracy, precision, and efficiency, highlighting the need for better approaches.The second contribution comes in the form of progress towards improving on the performance of established methods. For this, the thesis introduces the concept of "eigentropy" to quantify the geometric disorder or geometric information for points of a cloud. Eigentropy is conceptually similar to entropy as it appears in thermodynamics and information theory. Three novel methods are introduced to improve the performance of ICP-based scan-matching for loosely structured terrain. These methods are termed the: eigentropy filter, matching by normal deviation and unilateral eigentropy rejection, and they are based on the geometrical information content at a point. The results associated with these methods improve the overall performance of the ICP algorithm and five accurate, precise, efficient, and robust ICP variants are identified from an expanded population of 73,728. These five variants all use the methods introduced in this thesis.

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Is It What I Think It Is? Is It Where I Think It Is? Using Point-Clouds for Diagnostic Testing of a Digging Assembly's Form and Pose for an Autonomous Mining Shovel

2016

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This paper addresses the problem of verifying a control system's knowledge about the shape and pose of an electric mining shovel's digging assembly. The need for such verification arises in order to ensure safe autonomous operation. The likelihood of unintended collision is reduced by confirming that the digging assembly occupies the region of space it is thought to occupy. We present two methods for verification. The first computes the probability that regions of key interest have the geometric form expected, subject to an allowed uncertainty, given measured point-cloud data. The second computes a likelihood distribution over a family of possible hypotheses by considering the level of support each range measurement provides to each hypothesis. The ideas presented extend, with appropriate adaptation, to other applications where it is necessary to verify the knowledge that a control system may possess about regions of space that are occupied from instant-to-instant. C

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An Adaptive Structure Filter for Sensor Registration from Unstructured Terrain

Cited by 10 publications

References 24 publications

Determining and verifying object pose from LiDAR measurements to support the perception needs of an autonomous excavator

Determining and verifying object pose from LiDAR measurements to support the perception needs of an autonomous excavator

Scan matching for terrain mapping in open-pit mining

Is It What I Think It Is? Is It Where I Think It Is? Using Point-Clouds for Diagnostic Testing of a Digging Assembly's Form and Pose for an Autonomous Mining Shovel

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