Normal Distributions Transform Traversability Maps: LIDAR‐Only Approach for Traversability Mapping in Outdoor Environments

Ahtiainen, Juhana; Stoyanov, Todor; Saarinen, Jyrki

doi:10.1002/rob.21657

Cited by 44 publications

(50 citation statements)

References 47 publications

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“…In Section IV-A, we prove that the computation of the map parameters λ i according to (10) indeed maximizes the data likelihood.…”

Section: B Mappingmentioning

confidence: 89%

“…We prove that the map parameters λ i according to (10) maximize the likelihood of the underlying data by solving the following optimization problem: .…”

Section: A Decay-rate Maps Maximize the Data Likelihoodmentioning

confidence: 99%

“…Hence, the decay-rate map computed according to (10) is the most probable map given the sensor data.…”

Section: A Decay-rate Maps Maximize the Data Likelihoodmentioning

confidence: 99%

See 2 more Smart Citations

An Analytical Lidar Sensor Model Based on Ray Path Information

Schaefer

Lukas

Burgard

2017

IEEE Robot. Autom. Lett.

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Two core competencies of a mobile robot are to build a map of the environment and to estimate its own pose on the basis of this map and incoming sensor readings. To account for the uncertainties in this process, one typically employs probabilistic state estimation approaches combined with a model of the specific sensor. Over the past years, lidar sensors have become a popular choice for mapping and localization. However, many common lidar models perform poorly in unstructured, unpredictable environments, they lack a consistent physical model for both mapping and localization, and they do not exploit all the information the sensor provides, e.g. out-of-range measurements. In this paper, we introduce a consistent physical model that can be applied to mapping as well as to localization. It naturally deals with unstructured environments and makes use of both out-ofrange measurements and information about the ray path. The approach can be seen as a generalization of the well-established reflection model, but in addition to counting ray reflections and traversals in a specific map cell, it considers the distances that all rays travel inside this cell. We prove that the resulting map maximizes the data likelihood and demonstrate that our model outperforms state-of-the-art sensor models in extensive real-world experiments.

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“…In Section IV-A, we prove that the computation of the map parameters λ i according to (10) indeed maximizes the data likelihood.…”

Section: B Mappingmentioning

confidence: 89%

“…We prove that the map parameters λ i according to (10) maximize the likelihood of the underlying data by solving the following optimization problem: .…”

Section: A Decay-rate Maps Maximize the Data Likelihoodmentioning

confidence: 99%

See 1 more Smart Citation

An Analytical Lidar Sensor Model Based on Ray Path Information

Schaefer

Lukas

Burgard

2017

IEEE Robot. Autom. Lett.

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show abstract

“…Aside from ICP, Normal Distributions Transform (NDT) was introduced by Biber and Strasser in 2003 for scan matching and registration of laser-scan data. In NDT, the reference point cloud is transformed into fixed 2D cells and is converted to a set of Gaussian probability distribution, and then, scan data is matched to the set of normal distributions [40]. In other words, NDT is a grid-based representation that matches LiDAR data with the set of normal distributions rather than point cloud.…”

Section: Introductionmentioning

confidence: 99%

High Definition 3D Map Creation Using GNSS/IMU/LiDAR Sensor Integration to Support Autonomous Vehicle Navigation

İlçi

Toth

2020

Sensors

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Recent developments in sensor technologies such as Global Navigation Satellite Systems (GNSS), Inertial Measurement Unit (IMU), Light Detection and Ranging (LiDAR), radar, and camera have led to emerging state-of-the-art autonomous systems, such as driverless vehicles or UAS (Unmanned Airborne Systems) swarms. These technologies necessitate the use of accurate object space information about the physical environment around the platform. This information can be generally provided by the suitable selection of the sensors, including sensor types and capabilities, the number of sensors, and their spatial arrangement. Since all these sensor technologies have different error sources and characteristics, rigorous sensor modeling is needed to eliminate/mitigate errors to obtain an accurate, reliable, and robust integrated solution. Mobile mapping systems are very similar to autonomous vehicles in terms of being able to reconstruct the environment around the platforms. However, they differ a lot in operations and objectives. Mobile mapping vehicles use professional grade sensors, such as geodetic grade GNSS, tactical grade IMU, mobile LiDAR, and metric cameras, and the solution is created in post-processing. In contrast, autonomous vehicles use simple/inexpensive sensors, require real-time operations, and are primarily interested in identifying and tracking moving objects. In this study, the main objective was to assess the performance potential of autonomous vehicle sensor systems to obtain high-definition maps based on only using Velodyne sensor data for creating accurate point clouds. In other words, no other sensor data were considered in this investigation. The results have confirmed that cm-level accuracy can be achieved.

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“…In addition, to the best knowledge of the authors, there are no tagged repositories with this kind of data. As an alternative to manually-labelled data, learning from demonstration with 3D point clouds acquired from a teleoperated vehicle on traversable zones can be employed by a Positive Naive Bayes classifier [16], a Gaussian Process [17], or a support vector machine [18].…”

Section: Introductionmentioning

confidence: 99%

Supervised Learning of Natural-Terrain Traversability with Synthetic 3D Laser Scans

et al. 2020

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Autonomous navigation of ground vehicles on natural environments requires looking for traversable terrain continuously. This paper develops traversability classifiers for the three-dimensional (3D) point clouds acquired by the mobile robot Andabata on non-slippery solid ground. To this end, different supervised learning techniques from the Python library Scikit-learn are employed. Training and validation are performed with synthetic 3D laser scans that were labelled point by point automatically with the robotic simulator Gazebo. Good prediction results are obtained for most of the developed classifiers, which have also been tested successfully on real 3D laser scans acquired by Andabata in motion.

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Normal Distributions Transform Traversability Maps: LIDAR‐Only Approach for Traversability Mapping in Outdoor Environments

Cited by 44 publications

References 47 publications

An Analytical Lidar Sensor Model Based on Ray Path Information

An Analytical Lidar Sensor Model Based on Ray Path Information

High Definition 3D Map Creation Using GNSS/IMU/LiDAR Sensor Integration to Support Autonomous Vehicle Navigation

Supervised Learning of Natural-Terrain Traversability with Synthetic 3D Laser Scans

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