GPU based real-time SLAM of six-legged robot

Abstract

Vision and AHRS (attitude and heading reference system) sensors fusion strategy is prevalent in recent years for the legged robot's SLAM (Simultaneous Localization and Mapping), due to its low cost and effectiveness in the global positioning system. In this paper, a new adaptive estimation algorithm is proposed to achieve the robot SLAM by fusing binocular vision and AHRS sensors. A novel acceleration algorithm for SIFT implementation based on Compute Unified Device Architecture (CUDA) is presented to detect the matching feature points in 2D images. All the steps of SIFT were specifically distributed and implemented by CPU or GPU, according to the step's characteristics to make full use of computational resources. The registration of the 3D feature point cloud is performed by using the iterative closest point (ICP) algorithm. Our GPU-based SIFT implementation can run at the speed of 30 frames per second (fps) on most images with 900 × 750 resolution in the test. Compared to other methods, our algorithm is simple to implement and suitable for parallel processing. It can be easily integrated into mobile robot’s tasks like navigation or object tracking, which need the real-time localization information. Experiments results showed that in the unknown indoor environments, the proposed algorithm`s operation is stable and the positioning accuracy is high.