Place recognition, an algorithm to recognize the re-visited places, plays the role of back-end optimization trigger in a full SLAM system. Many works equipped with deep learning tools, such as MLP, CNN, and transformer, have achieved great improvements in this research field. Point cloud transformer is one of the excellent frameworks for place recognition applied in robotics, but with large memory consumption and expensive computation, it is adverse to widely deploy the various point cloud transformer networks in mobile or embedded devices. To solve this issue, we propose a binary point cloud transformer for place recognition. As a result, a 32-bit full-precision model can be reduced to a 1-bit model with less memory occupation and faster binarized bitwise operations. To our best knowledge, this is the first binary point cloud transformer that can be deployed on mobile devices for online applications such as place recognition. Experiments on several standard benchmarks demonstrate that the proposed method can get comparable results with the corresponding full-precision transformer model and even outperform some full-precision deep learning methods. For example, the proposed method achieves 93.28% at the top @1% and 85.74% at the top @1% on the Oxford RobotCar dataset in terms of the metric of the average recall rate. Meanwhile, the size and floating point operations of the model with the same transformer structure reduce 56.1% and 34.1% respectively from original precision to binary precision.

翻译：点云变压器是用于机器人中定位的绝佳框架之一,但随着大量记忆消耗和昂贵的计算,它不利于在移动或嵌入设备中广泛部署各种点云变压器网络。为了解决这个问题,我们提议了一个二进点云变压器,以便进行定位。结果,32比特的全精度模型可以降低为1比特的模型,减少记忆占用和更快的二进位操作。据我们所知,这是第一个二进点云变压器,可以在移动设备上安装,例如地点识别,但是由于大量记忆消耗和昂贵的计算,因此不利于在移动或嵌入设备中广泛部署各种点云变压器网络。为了解决这个问题,我们提议了一个二进点云变压器,用于定位。因此,一个32比特的全精度模型可以降为1比特的模型,而减少记忆占用率和更快的二进位化的操作器。例如,一个32比特的完全精度模型可以降低到一个1比特的模型的精确度模型。,在最高一级,351 和最高版本的模型中位标准值为351 标准的模型,可以分别将351 。</s>