[9-10]中的作者应用粒子滤波的思想解决SLAM问题，提出了Rao-

The author in [9-10] applied the idea of ​​particle filtering to solve the SLAM problem, and proposed the Rao-Blackwellised particle filter (RBPF). Based on the Rao-Blackwellised particle filter, the author in [11-13] developed Fast-SLAM1.0, Fast-SLAM2.0 by proposing a distribution that more closely approximates the real posterior distribution and adopting adaptive resampling technology. And achieved good results. Although the filtering technology can quickly solve the SLAM problem, a large amount of data is discarded during the calculation process, and a global consistent map cannot be guaranteed when constructing a global map.

The author in 9-10 uses the idea of particle filtering to solve the SLAM problem and proposes the Rao-Blackwellised particle filter (RBPF). Based on the Rao-Blackwellised particle filter, the authors in the paper developed Fast-SLAM 1.0, Fast-SLAM 2.0 and achieved good results by proposing a proposed distribution that is more close to the true post-mortem distribution and using adaptive resampling technology. Although the application filtering technology can solve slam problems quickly, during the operation, large amounts of data are discarded, and the global map can not be guaranteed to obtain global consistency when building the global map.

In [9-10], the author uses the idea of particle filter to solve SLAM problem and proposes Rao Blackwellised particle filter (RBPF). On the basis of Rao Blackwellised particle filter, the author of [11-13] developed fast-slam1.0 and fast-slam2.0 by proposing a more realistic posterior distribution and adopting adaptive resampling technology. Although the SLAM problem can be solved quickly by using filtering technology, a large amount of data is discarded in the process of operation, which makes it impossible to obtain a global consistent map when constructing a global map.<br>