Concat on non-identical categories in categorical indexes raises TypeError
#### Code Sample, a copy-pastable example if possible
```
import pandas as pd
a = pd.DataFrame({'one':['foo','bar','bar'],'two':[1,2,3],'three':[1,3,4]})
b = pd.DataFrame({'one':['baz','bar','plob'],'two':[2,2,1],'three':[5,3,1]})

a['one'] = a['one'].astype('category')
b['one'] = b['one'].astype('category')
c = a.set_index('one')
d = b.set_index('one')
print(pd.concat([a, b]))
    one  three  two
0   foo      1    1
1   bar      3    2
2   bar      4    3
0   baz      5    2
1   bar      3    2
2  plob      1    1
print(pd.concat([c, d]))
raise TypeError("categories must match existing categories "
TypeError: categories must match existing categories when appending
```
#### Problem description
While concatenating categorical data with non-identical categories  is now support for data columns  by upcasting the data to an appropriate dtype it is still throwing  an error when attempting to do the same thing with categorical indexes.
I think it would be convenient for users to implement the same behaviour for data columns and indexes.

#### Expected Output
```
print(pd.concat([c, d]))
      three  two
one             
foo       1    1
bar       3    2
bar       4    3
baz       5    2
bar       3    2
plob      1    1
print(pd.concat([c, d]).index.dtype)
dtype('O')
```
#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.2.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_GB.UTF-8
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8

pandas: 0.20.3
pytest: 3.2.1
pip: None
setuptools: None
Cython: 0.26
numpy: 1.13.1
scipy: 0.19.1
xarray: None
IPython: 6.1.0
sphinx: 1.6.3
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: None
numexpr: 2.6.2
feather: None
matplotlib: 2.0.2
openpyxl: 2.4.8
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 0.9.8
lxml: 3.8.0
bs4: 4.6.0
html5lib: 0.999
sqlalchemy: 1.1.13
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: None

</details>
