Point cloud refinement with self‐calibration of a mobile multibeam lidar sensor

Nouira, Houssem; Deschaud, Jean-Emmanuel; Goulette, François

doi:10.1111/phor.12198

Cited by 2 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The moving ranging sensor registration problem has been addressed several times with solutions broadly falling into two categories: (a) Methods that use a model of the world as a reference, solving the registration problem by finding the sensor pose that best fits sensor measurements to the model from several different sensor locations (Duff, 2006;Gao, Sands, & Spletzer, 2010;Kurz, Buckley, Howell, & Schneider, 2011;Phillips, Green, & McAree, 2015;Underwood, Hill, & Scheding, 2007;Williams, Upcroft, Denman, Reid, & McAree, 2009); and (b) methods that solve the registration problem by making use of correlations in point clouds acquired from different locations of the sensor (Chan & Lichti, 2013;Elseberg, Borrmann, & Nüchter, 2013;Levinson & Thrun, 2014;Maddern, Harrison, & Newman, 2012;Nouira, Deschaud, & Goulette, 2017;Sheehan, Harrison, & Newman, 2012;Chan, Lichti, & Belton, 2015). We will call the first model-based registration methods and the second model-free methods.…”

mentioning

confidence: 99%

Registration of three‐dimensional scanning LiDAR sensors: An evaluation of model‐based and model‐free methods

D’Adamo

Phillips

McĂree

2018

Journal of Field Robotics

View full text Add to dashboard Cite

Registration, also know as extrinsic calibration, is the process of determining the position and orientation of a sensor relative to a known frame of reference. For ranging sensors such as light detection and ranging (LiDAR) used in field robotic applications, the quality of the registration determines the utility of the range measurements. This paper makes two contributions. The first is the introduction of a new method, termed maximum sum of evidence (MSoE) for registering three‐dimensional LiDAR sensors to moving platforms. This method is shown to produce more accurate registration solutions than two leading methods for these sensors, the adaptive structure registration filter (ASRF) and Rényi quadratic entropy (RQE). The second contribution of the paper is to study the accuracy of the MSoE registration against these two other approaches. One of these, like the MSoE, requires a truth model of the environment. The other, a model‐free method, seeks the registration that minimizes the RQE of a compound point cloud. The main finding of this investigation is that while the model‐based methods prove more accurate than the model‐free approach, the results of all three methods are fit for their intended field robotic applications. This leads us to conclude that registration based on RQE is preferable in many, if not all, field robotic applications for reasons of convenience, since a truth model of the environment is not required.

show abstract