Efficient GPU-based construction of occupancy grids using several laser range-finders

International Journal of Vehicle Autonomous Systems

Volumn 6, Issue 1-2, 2008, Pages 48-83

  Yguel, Manuel ^a   Aycard, Olivier ^a   Laugier, Christian ^a  

^a INRIA RHÔNE ALPES   (France)

Author keywords

GPU; graphical processor unit; laser range finder; occupancy grid; sensor model

Indexed keywords

EID: 84455178066     PISSN: 14710226     EISSN: None     Source Type: Journal    
DOI: 10.1504/IJVAS.2008.016478     Document Type: Article

References (27)

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18. It is only necessary for the sensors that view the cell.
19. For a certain variable V we will note in capital case the variable, in normal case v one of its realisation, and we will note p(v) for P([V = v]) the probability of arealisation of the variable.
20. which is a more general modelling than the uniform choice made in Elfes (1989a).
21. Here we suppose that z is an integer which represents the cell index, which the sensor measurement corresponds to: if z is real it is ∣_zj +1.
22. Here we assume that k is the index of the cell which represents all the points with radial coordinate in [k — 1; k], that is, we assume a length of 1 for cell, for simplicity.
23. In this case a cell is considered as occupied if the probability is greater than 0.5.
24. In the cited implementation the constant is the same for the area before and at the obstacle but it is easy to show that with the sensor model described here to equalise γι and γ1 leads to a negative prior.
25. Here, we consider, for the integral, the Lebesgue measure for simplicity, but the formalism is general as soon as the measure of the intersection between any face of A and any face of B is well defined.
26. The complexity of the optimal algorithm (Balaban, 1995) that solves this problem is O(n log(n) + k) in time and O(n) in space where n is the sum of the numbers of segments in both subdivision A and B while k is the number of intersection points in both subdivisions. In the case of simply connected subdivisions the optimal complexity is O(n + k) in time and space (Finke and Hinrichs, 1995), and for convex subdivisions the optimal complexity is O(n + k) in time and O(n) in space (Guibas and Seidel, 1986).
27. In a slam perspective, for example.