Descriptor Distillation for Efficient Multi-Robot SLAM

Performing accurate localization while maintaining the low-level communication bandwidth is an essential challenge of multi-robot simultaneous localization and mapping (MR-SLAM). In this paper, we tackle this problem by generating a compact yet discriminative feature descriptor with minimum inference time. We propose descriptor distillation that formulates the descriptor generation into a learning problem under the teacher-student framework. To achieve real-time descriptor generation, we design a compact student network and learn it by transferring the knowledge from a pre-trained large teacher model. To reduce the descriptor dimensions from the teacher to the student, we propose a novel loss function that enables the knowledge transfer between two different dimensional descriptors. The experimental results demonstrate that our model is 30% lighter than the state-of-the-art model and produces better descriptors in patch matching. Moreover, we build a MR-SLAM system based on the proposed method and show that our descriptor distillation can achieve higher localization performance for MR-SLAM with lower bandwidth.