Hi there!

I’m here since I find Julia, and JuMP in particular, very impressive and I suspect it should be a very good fit for SLAM (Simultaneous Localization and Mapping), where C++ is still the popular choice. In particular, I think Julia would be ideal to create programs and algorithms that are both performant and readable/learnable.

Now, I’m trying my hand at implementing a basic part of many SLAM algorithms, which is bundle adjustment. I read a bunch of papers and, long story short, I’m trying to use JuMP to solve the problem:

\underset{\mathbf{C} ,\ \mathbf{W}}{\min}\sum\nolimits ^{n_{c}}_{i=1}\sum\nolimits ^{n_{p}}_{j=1} d^{2}(\mathbf{F}_{i,j} ,\mathbf{C}_{i}\mathbf{W}_{j})

where:

- \mathbf{F}_{i,j} = projection of j-th 3D point to the i-th camera
- \mathbf{C}_{i} = i-th camera matrix (composition of the calibration matrix \mathbf{K}, and the camera’s own rotation and translation)
- \mathbf{W}_j = j-th 3D point in world coordinate frame, homogeneous coordinates

I modeled this problem in JuMP as the following non-linear objective (all `cam`

* and `wpoints`

are `@variable`

s):

```
@variable(model, cost)
@NLconstraint(model, cost ==
sum(distance_sq(
matches[j, i, :],
project(
camfl[i],
campp[i,1], campp[i,2],
camloc[i,:],
camrot[i,:],
wpoints[j,:]))
for i = 1:ncameras
for j = 1:npoints))
@NLobjective(model, Min, cost)
```

The code is in this Gist.

The problem is that I get the following error:

Unexpected array JuMP.VariableRef[camloc[1,1], camloc[1,2], camloc[1,3]] in nonlinear expression. Nonlinear expressions may contain only scalar expressions.

As far as I can tell, I’m using the “auxiliary variable + constraint”, like the documentation suggests (it also mentions splatting, but I don’t see how it’s applicable).

So… what am I getting wrong? Can I express the optimization objective in any other way?

I should mention that I’m only experienced in computer engineering, and my knowledge of geometry and mathematical optimization is rusty.

I realize some extra context that I forgot to add might be necessary to understand the problem; of course, I’m available to clarify anything as soon as I can.

Thank you very much in advance. Keep up the (very) good work!