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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Voi. XXXVII. Part B5. Beijing 2008 
Figure 4. Personal navigator: 1) calibration mode data flow is 
shown in solid lines; and 2) when GPS signals are not available, 
the dotted lines become solid indicating that now the navigation 
solution is formed based on calibrated data of dead reckoning 
sensors, including the human locomotion model parameter, step 
length (SL) and step direction (SD). 
3. PERFORMANCE ASSESSMENT 
The personal navigator in the hybrid and DR modes has been 
extensively tested using various operators, different terrain 
configuration and mixed outdoor-indoor environments. The 
details of the performance test to date were provided in 
(Grejner-Brzezinska et al., 2006a-c; 2007a and b; Moafipoor 
2007a and b; Toth et al., 2007), and only summary statistics are 
presented here, with the emphasis on the newest results of the 
mixed indoor-outdoor setting. 
In this experiment, data were collected in the parking lot and 
inside the Center for Mapping building on August 21 and 26, 
2007. The operators, S and E, walked the parking lot and 
hallways of the Center for Mapping, and made several loops 
following the marked control points in the hallways of this 
single-storey building. A floor plan of the building was 
previously acquired by classical surveying methods, and control 
points were established in the hallways with the accuracy better 
than 1-2 cm in E and N, and 5 mm in height. The main objective 
of the control points was to facilitate the prediction of the user’s 
position and provide control for the reference trajectory inside 
the building where no GPS was available. By the time the 
operators started walking inside the building, they had 
completed outside calibration procedures during normal GPS 
signal availability (using DD carrier phase and pseudorange 
measurements), which was required for a better performance of 
the other sensors (see Figure 5). 
Inside the building, the heading was estimated from the 
HMR3000 magnetometer compass and HG1700 gyro. The 
altitude was measured by the PTB220 barometer, which was 
calibrated against the known pressure standards (pressure, 
temperature, etc.) for the general area of activity. It was 
observed that after completing the initial calibration, these 
sensors showed performance that ensured redundant and 
complementary measurement inputs, as well as sufficient 
stability along the trajectories studied here. 
Figure 5a. Center for Mapping floor plan and DR trajectory 
reconstruction for operator S using compass heading. 
Figure 5b. Center for Mapping floor plan and DR trajectory 
reconstruction for operator E using compass heading. 
Tables 7 and 8 show the accuracy assessment of the indoor DR 
trajectory for one full loop along the Center for Mapping 
hallways, and Table 9 provides the statistics of the three 
complete indoor loops. This test represents the combination of 
outdoor and indoor environments; 350 s of outdoor sensor 
calibration was followed by three complete indoor loops in 
three minutes (Aug. 26 dataset), using gyro/compass heading. 
As can be seen in Table 9, three indoor loops are still viable 
within the 3-5 m CEP50 constraint. 
Test 
data set 
SL 
model 
Mean 
[m] 
Std 
[m] 
Max 
[m] 
End 
Misclosure 
[m] 
CEP 
(50%) 
M 
Operator 
S 
Fuzzy 
0.78 
0.87 
1.61 
2.18 
0.49 
ANN 
1.24 
0.75 
1.88 
2.14 
1.17 
Operator 
E 
Fuzzy 
0.84 
0.81 
1.95 
2.75 
0.73 
ANN 
0.80 
0.56 
1.45 
1.94 
0.77 
Table 7. Statistical fit to reference trajectory of the indoor DR 
trajectories generated using SL predicted with fuzzy logic and 
ANN, and compass heading; one indoor loop.