Add post on quaternions

bib/bibliography.bib

@@ -288,3 +288,72 @@
   address =	 {Cambridge, Massachusetts},
   isbn =	 9780262039420,
 }
+
+@Book{stillwell2010_mathem_its_histor,
+  author =	 {John Stillwell},
+  title =	 {Mathematics and Its History},
+  year =	 2010,
+  publisher =	 {Springer},
+  url =		 {https://doi.org/10.1007/978-1-4419-6053-5},
+  DATE_ADDED =	 {Fri Nov 6 14:39:47 2020},
+  doi =		 {10.1007/978-1-4419-6053-5},
+  isbn =	 9781441960528,
+  series =	 {Undergraduate Texts in Mathematics},
+}
+
+@article{sola2017_quater_kinem_error_state_kalman_filter,
+  author =	 {Sol{\`a}, Joan},
+  title =	 {Quaternion Kinematics for the Error-State Kalman
+                  Filter},
+  journal =	 {CoRR},
+  year =	 {2017},
+  url =		 {http://arxiv.org/abs/1711.02508v1},
+  abstract =	 {This article is an exhaustive revision of concepts
+                  and formulas related to quaternions and rotations in
+                  3D space, and their proper use in estimation engines
+                  such as the error-state Kalman filter.  The paper
+                  includes an in-depth study of the rotation group and
+                  its Lie structure, with formulations using both
+                  quaternions and rotation matrices. It makes special
+                  attention in the definition of rotation
+                  perturbations, derivatives and integrals. It
+                  provides numerous intuitions and geometrical
+                  interpretations to help the reader grasp the inner
+                  mechanisms of 3D rotation.  The whole material is
+                  used to devise precise formulations for error-state
+                  Kalman filters suited for real applications using
+                  integration of signals from an inertial measurement
+                  unit (IMU).},
+  archivePrefix ={arXiv},
+  eprint =	 {1711.02508},
+  primaryClass = {cs.RO},
+}
+
+@article{welchIntroductionKalmanFilter2006,
+  title = {An {{Introduction}} to the {{Kalman Filter}}},
+  volume = {7},
+  issn = {10069313},
+  url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.79.6578&rep=rep1&type=pdf},
+  doi = {10.1.1.117.6808},
+  abstract = {In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discrete-data linear filtering problem. Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of extensive research and application, particularly in the area of autonomous or assisted navigation. The Kalman filter is a set of mathematical equations that provides an efficient computational (recursive) means to estimate the state of a process, in a way that minimizes the mean of the squared error. The filter is very powerful in several aspects: it supports estimations of past, present, and even future states, and it can do so even when the precise nature of the modeled system is unknown. The purpose of this paper is to provide a practical introduction to the discrete Kalman filter. This introduction includes a description and some discussion of the basic discrete Kalman filter, a derivation, description and some discussion of the extended Kalman filter, and a relatively simple (tangible) example with real numbers \& results.},
+  number = {1},
+  journaltitle = {In Practice},
+  date = {2006},
+  pages = {1--16},
+  author = {Welch, Greg and Bishop, Gary},
+  file = {/home/dimitri/Nextcloud/Zotero/storage/LJ7QQCXF/Bishop, Welch - Unknown - An Introduction to the Kalman Filter.pdf},
+  eprinttype = {pmid},
+  eprint = {20578276}
+}
+
+@inproceedings{joldes2020_algor_manip_quater_float_point_arith,
+  author =	 {M. {Jolde{\c{s}}} and J. -M. {Muller}},
+  title =	 {Algorithms for Manipulating Quaternions in
+                  Floating-Point Arithmetic},
+  booktitle =	 {2020 {IEEE} 27th Symposium on Computer Arithmetic
+                  {(ARITH)}},
+  year =	 2020,
+  pages =	 {48-55},
+  doi =		 {10.1109/ARITH48897.2020.00016},
+  url =		 {https://doi.org/10.1109/ARITH48897.2020.00016},
+}