diff --git a/src/site/markdown/tutorials/direct-location-with-DEM.md b/src/site/markdown/tutorials/direct-location-with-DEM.md
index b01453f868d014e2cfa39e74685a1bc4579b04ba..8f6bc1f0ad7fce7f1da6e5f7a1d6cacc727b09b8 100644
--- a/src/site/markdown/tutorials/direct-location-with-DEM.md
+++ b/src/site/markdown/tutorials/direct-location-with-DEM.md
@@ -114,8 +114,6 @@ Initialize Rugged with these parameters :
satellitePVList, 6, CartesianDerivativesFilter.USE_P,
satelliteQList, 8, AngularDerivativesFilter.USE_R);
-
-
## Computing a direct location grid
In a similar way as in the first tutorial [direct location](./direct-location.html), we call Rugged direct location method. This time it is called in a loop so as to generate a full grid on disk.
@@ -124,8 +122,9 @@ In a similar way as in the first tutorial [direct location](./direct-location.ht
int lineStep = (maxLine - minLine) / nbLineStep;
int pxStep = (maxPx - minPx) / nbPxStep;
- List<GeodeticPoint> pointList = new ArrayList<GeodeticPoint>();
- for (int i = 0; i < nbLineStep; i++) {
+ for (int i = 0; i < nbLineStep; i++) {
+
+ List<GeodeticPoint> pointList = new ArrayList<GeodeticPoint>(nbPxStep);
int currentLine = minLine + i * lineStep;
for (int j = 0; j < nbPxStep; j++) {
int currentPx = minPx + j * pxStep;
@@ -133,17 +132,14 @@ In a similar way as in the first tutorial [direct location](./direct-location.ht
Vector3D position = lineSensor.getPosition();
AbsoluteDate currentLineDate = lineSensor.getDate(currentLine);
Vector3D los = lineSensor.getLos(absDate, currentPx);
- GeodeticPoint point = rugged.directLocation(currentLineDate, position, los);
-
- // Write latitude
+ pointList.add(rugged.directLocation(currentLineDate, position, los));
+ }
+ for (GeodeticPoint point : pointList) {
if (point != null) {
dos.writeFloat((float) FastMath.toDegrees(point.getLatitude()));
} else {
dos.writeFloat((float) base);
}
- // Store the GeodeticPoint to write longitude and altitude later
- pointList.add(point);
-
}
for (GeodeticPoint point : pointList) {
if (point != null) {
@@ -159,7 +155,7 @@ In a similar way as in the first tutorial [direct location](./direct-location.ht
dos.writeFloat((float) base);
}
}
-
+ }
## Source code
The source code is available in DirectLocationGrid.java
diff --git a/src/site/markdown/tutorials/direct-location.md b/src/site/markdown/tutorials/direct-location.md
index 56c3c9aebe55264375216154ff26040a2677b5b7..4253c9c5dc4bf5df1122d3f734828a2196615691 100644
--- a/src/site/markdown/tutorials/direct-location.md
+++ b/src/site/markdown/tutorials/direct-location.md
@@ -21,9 +21,8 @@ list of positions, velocities and attitude quaternions recorded during the acqui
passing all this information to Rugged, we will be able to precisely locate each point of
the image on the Earth. Well, not exactly precise, as this first tutorial does not use a
Digital Elevation Model, but considers the Earth as an ellipsoid. The DEM will be added in
-a second tutorial [Direct location with DEM](./direct-location-with-DEM.html). The objective
-here is limited to explain how to initialize everything Rugged needs to know about the sensor
-and the acquisition.
+a second tutorial [Direct location with DEM](./direct-location-with-DEM.html). The objective here is limited to explain how to initialize everything
+Rugged needs to know about the sensor and the acquisition.
## Sensor's definition
@@ -199,22 +198,22 @@ Finally we can initialize Rugged. It looks like this:
Argh, that sounds complicated. It is not so difficult since we have already defined most of what is
needed. Let's describe the arguments line by line:
-
+
The first 3 are related to the DEM. Since we are ignoring the DEM, they can be set respectively to
`null`, `0` and `AlgorithmId.IGNORE_DEM_USE_ELLIPSOID`.
-The next 3 arguments defines the ellipsoid and the reference frames: `EllipsoidId.WGS84`,
+The next 3 arguments define the ellipsoid and the reference frames: `EllipsoidId.WGS84`,
`InertialFrameId.EME2000`, `BodyRotatingFrameId.ITRF`
On the third line, we find the time interval of the acquisition: acquisitionStartDate, acquisitionStopDate,
tStep (step at which the pre-computed frames transforms cache will be filled), timeTolerance (margin
-allowed for extrapolation, in seconds). This is an important information as Rugged
+allowed for extrapolation during inverse location, in seconds. A few lines must be taken into account like: timeTolerance = 5/lineSensor.getRate(0)). This is an important information as Rugged
will pre-compute a lot of stuff at initialization in order to optimize further calls to the direct and
inverse location functions.
On the fourth line, the arguments are the list of time-stamped positions and velocities as well as options
-for interpolation: number of points to use and type of filter for derivatives. We find the same arguments
-on the last line for the attitude quaternions.
+for interpolation: number of points to use and type of filter for derivatives. The interpolation polynomials for nbPVPoints without any derivatives (case of CartesianDerivativesFilter.USE_P: only positions are used, without velocities) have a degree nbPVPoints - 1. In case of computation with velocities included (case of CartesianDerivativesFilter.USE_PV), the interpolation polynomials have a degree 2*nbPVPoints - 1. If the positions/velocities data are of good quality and spaced by a few seconds, one may choose only a few points but interpolate with both positions and velocities; in other cases, one may choose more points but interpolate only with positions.
+We find the same arguments on the last line for the attitude quaternions.
The sensor models are added after initialization. We can add as many as we want.