BotVIO: A Lightweight Transformer-Based Visual-Inertial Odometry for Robotics

Wenhui Wei, Yangfan Zhou, Yimin Hu, Zhi Li, Sen Wang, Xin Liu, Jiadong Li

Abstract: Visual–inertial odometry (VIO) provides a robust localization solution for simultaneous localization and mapping systems. Self-supervised VIO, a leading approach, has the advantage of not requiring extensive ground-truth labels. Regrettably, this method still poses challenges for robotic applications, particularly uncrewed aerial vehicles, due to its computational complexity arising from inadequate model designs. To address this bottleneck, we introduce BotVIO (where “Bot” refers to “robotics”), a transformer-based self-supervised VIO model, offering an excellent solution to alleviate computational burdens for robotics. Our lightweight backbone combines shallow CNNs with spatial–temporal-enhanced transformers to replace conventional architectures, while the minimalist cross-fusion module uses single-layer cross-attention to enhance multimodal interaction. Extensive experiments show that, during pose estimation, BotVIO achieves a remarkable 70.37% reduction in trainable parameters and a 74.85% decrease in inference speed, reaching up to 57.80 fps on an NVIDIA Jetson NX (10W&2CORE), while improving pose accuracy and robustness. For the benefit of the community, we make public the source code.1

External IDs:dblp:journals/trob/WeiZHLWLL25