PHOTOGRAMMETRY-DERIVED NAVIGATION PARAMETERS FOR INS 
KALMAN FILTER UPDATES 
F. A. Bayoud, J. Skaloud, B. Merminod 
Geodetic Engineering Laboratory (TOPO), Swiss Federal Institute of Technology/Lausanne (EPFL) 
Bât. GR, CH 1015, Switzerland — (fadiatef. bayoud, jan.skaloud, bertrand.merminod)@epfl.ch 
Commission V, WG V/2 
KEY WORDS: Mobile Mapping; Navigation; Robotics; Integration; IMU/INS; Kalman Filter. 
ABSTRACT: 
In this paper, a GPS-independent mobile navigation and mapping system is introduced. This system employs the 
photogrammetric intersection to determine the coordinates of the surrounding objects and then it uses these newly 
known objects to compute its own position, when it moves, by the photogrammetric resection. The photogrammetric 
resection output — the exterior orientation parameters — is then used as external measurements in an INS Kalman Filer 
to compute the filtered exterior orientation parameters to do the intersection. This methodology of navigation and 
mapping is applied in the robotics community using different sensors and methods, where it is called Simultaneous 
Localisation And Mapping (SLAM); SLAM is the problem of mapping an area and at the same time using the map to 
locate the robot. In this paper, the solution for SLAM by integrating photogrammetry resection output and INS via a 
Kalman Filer is described. 
1. INTRODUCTION 
It is fundamental that mobile mapping systems use 
two independent components; one is for positioning, 
and the other is for mapping. Looking closely at the 
mapping component, one can find that it might also 
be used for positioning. This idea is extensively used 
in the robotics community. 
Robotics Simultaneous Localisation And Mapping 
(SLAM) is the problem of mapping the environment 
surrounding the robot and at the same time using this 
map to determine the location of the robot (Csorba. 
1997; Newman, 1999). Currently, indoor robotics 
SLAM is solved using LASER scanners to locate the 
robot relative to the structured environment and at the 
same time to map this environment. LASER scanners 
showed to be a very good tool where the accuracy of 
localisation is within the centimetre level for short 
distances and favourable geometry. Outdoors robotics 
SLAM is not feasible with LASER scanners due to 
the absence of simple geometric environment. 
Therefore, different tools have to be used. 
Recently, the use of visual methods, integrated with 
Inertial Measurement Units (IMU), has gained 
interest. These visual methods rely on one or more 
cameras (or video). Yet, no clear indication about the 
mapping methods and integration algorithms is 
clearly illustrated in the relevant literature (Journal of 
Robotics Systems; V. 21; Issues 1 and 2). 
In Geomatics Engineering, navigation and mapping 
have been two central research topics for decades. A 
classical tool for mapping is Photogrammetry, where 
sequence of stereo-images are captured and analysed. 
As for navigation systems, the coupling of inertial 
sensors and Global Navigation Satellite Systems is 
indispensable. In addition, photogrammetric resection 
can also be used for positioning. 
In this paper, a mobile mapping system is introduced 
— independent of the GPS/INS integration - by 
employing two CCD cameras and one IMU (Figure 
1). The two CCDs will be used for mapping through 
photogrammetric intersection, and for positioning 
through photogrammetric resection. The presence of 
an IMU is significant in cases where images cannot 
be used for reasons of visibility and when certain 
manoeuvres do not guarantee knowledge of the 
environment; e.g. when the robot or vehicle captures 
images of an unknown environment. 
Thus, The relation. between photogrammetry and 
SLAM is, analysed in this study. This relation has not 
gained much attention due to the fact that SLAM 
requires full automation, which until recently was far 
from photogrammetry. Many attempts were directed 
towards the full automation of photogrammetry. This 
goal has been still falling short due to the need of 
high level of artificial intelligence. Having in mind 
that it is only a matter of time to reach full 
automation, an investigation on SLAM from the 
Geomatics point of view using navigation and 
mapping modus operandi is essential. 
  
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Figure 1: The system 
   
   
  
  
    
   
  
  
  
  
   
   
  
  
  
  
  
  
   
   
  
   
   
   
   
   
   
  
   
   
  
  
   
   
   
  
   
   
   
   
   
   
  
  
    
  
  
  
  
   
  
  
  
  
  
  
  
    
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