The Canadian planetary emulation terrain 3D mapping dataset

Tong, Chi Hay; Gingras, David R.; Larose, Kevin; Barfoot, Timothy D.; Dupuis, Érick

doi:10.1177/0278364913478897

Cited by 49 publications

(27 citation statements)

References 7 publications

(7 reference statements)

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“…Recently, a set of benchmark problems and datasets have become available, for example, the Radish dataset repository and the dataset papers published in the International Journal of Robotics Research, making it possible to follow the standard scientific practice to evaluate the different SLAM algorithms. [63][64][65][66] Given that SLAM front-end tends to very much sensor and robot specific, the focus of this section is on the backend that solves the estimation or the optimization problem.…”

Section: Criteria For Evaluating the Performance Of Slam Algorithmsmentioning

confidence: 99%

A critique of current developments in simultaneous localization and mapping

Huang

Dissanayake

2016

International Journal of Advanced Robotic Systems

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The number of research publications dealing with the simultaneous localization and mapping problem has grown significantly over the past 15 years. Many fundamental and practical aspects of simultaneous localization and mapping have been addressed, and some efficient algorithms and practical solutions have been demonstrated. The aim of this paper is to provide a critical review of current theoretical understanding of the fundamental properties of the SLAM problem, such as observability, convergence, achievable accuracy and consistency. Recent research outcomes associated with these topics are briefly discussed together with potential future research directions.

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Section: Criteria For Evaluating the Performance Of Slam Algorithmsmentioning

confidence: 99%

A critique of current developments in simultaneous localization and mapping

Huang

Dissanayake

2016

International Journal of Advanced Robotic Systems

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“…A series of field tests running the fully implemented ASAS architecture were conducted on the Mars emulation terrain at the CSA headquarters (Figure ; Tong, Gingras, Larose, Barfoot, & Dupuis, ) in St. Hubert, Quebec, Canada. The testing yard was augmented with additional terrain features, namely, higher sand, gravel, and bedrock slopes, as shown in Figure .…”

Section: Field Testing To Evaluate Asas Performancementioning

confidence: 99%

“…The Canadian Space Agency Mars emulation terrain. Photograph by Équation Groupe Conseil, Inc. (Tong et al, ) [Color figure can be viewed at wileyonlinelibrary.com]…”

Section: Field Testing To Evaluate Asas Performancementioning

confidence: 99%

Data‐driven mobility risk prediction for planetary rovers

Skonieczny

Shukla

Faragalli

et al. 2018

Journal of Field Robotics

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Mobility assessment and prediction continues to be an important and active area of research for planetary rovers, with the need illustrated by multiple examples of high slip events experienced by rovers on Mars. Despite slip versus slope being one of the strongest and most broadly used relationships in mobility prediction, this relationship is nonetheless far from precisely predictable. Although the literature has made significant advances in the predictability of average mobility, the other key related aspect of the problem is the risk caused by edge cases. A key contribution of this study is a metric for explicitly assessing mobility risk based on data-driven nonparametric slip versus slope relationships. The data-driven approach is meant to address limitations of past model-based approaches. The metric is informed by past work in terramechanics relating drawbar pull (i.e., net traction) to slip: High slip fraction (HSF), defined as the proportion of slip data points above 20%. Another contribution is a low complexity mobility prediction framework, the autonomous soil assessment system. Field tests demonstrate that, for sand and gravel, rover trafficability becomes nonlinear and highly variable above the 20% slip threshold.HSF is shown to be a useful metric for categorizing rover-terrain interactions into low, medium, or high risk, correctly and consistently. Furthermore, the metric is shown to be useful for early detection of potentially hazardous changes in roverterrain conditions. The combination of HSF with an appropriately sized queue structure for modeling slip versus slope enables an appropriate balance between responsiveness and stability. K E Y W O R D S mobility prediction, planetary robotics, rover slip

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“…Data sets based on camera vision data such as [1], [2], [3] and [4] are used to develop various applications such as visual odometry, semantic segmentation, and vehicle detection. Data sets based on LiDAR data such as [4], [5], [6], [7] and [8] are used in applications such as object detection, LiDAR odometry, and 3D mapping. However, most data sets do not focus on highly complex urban environments (significantly wide roads, lots of dynamic objects, GPS blackout regions and high-rise buildings) where actual autonomous vehicles operate.…”

Section: Introductionmentioning

confidence: 99%

Complex Urban LiDAR Data Set

Jeong

Cho

Shin

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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Fig. 1: This paper provides the complex urban data set including metropolitan area, apartment building complex and underground parking lot. Sample scenes from the data set can be found in https://youtu.be/IguZjmLf5V0.Abstract-This paper presents a Light Detection and Ranging (LiDAR) data set that targets complex urban environments. Urban environments with high-rise buildings and congested traffic pose a significant challenge for many robotics applications. The presented data set is unique in the sense it is able to capture the genuine features of an urban environment (e.g. metropolitan areas, large building complexes and underground parking lots). Data of two-dimensional (2D) and threedimensional (3D) LiDAR, which are typical types of LiDAR sensors, are provided in the data set. The two 16-ray 3D LiDARs are tilted on both sides for maximal coverage. One 2D LiDAR faces backward while the other faces forwards to collect data of roads and buildings, respectively. Raw sensor data from Fiber Optic Gyro (FOG), Inertial Measurement Unit (IMU), and the Global Positioning System (GPS) are presented in a file format for vehicle pose estimation. The pose information of the vehicle estimated at 100 Hz is also presented after applying the graph simultaneous localization and mapping (SLAM) algorithm. For the convenience of development, the file player and data viewer in Robot Operating System (ROS) environment were also released via the web page. The full data sets are available at: http://irap.kaist.ac.kr/dataset. In this website, 3D preview of each data set is provided using WebGL.

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The Canadian planetary emulation terrain 3D mapping dataset

Cited by 49 publications

References 7 publications

A critique of current developments in simultaneous localization and mapping

A critique of current developments in simultaneous localization and mapping

Data‐driven mobility risk prediction for planetary rovers

Complex Urban LiDAR Data Set

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