Abstract: Visual SLAM algorithm based on point features cannot estimate camera motion reliably， because it cannot extract point features sufficiently in weak texture environments. Edge features have richer environmental information than point features. However， they will affect the real-time performance of the system by introducing edge features directly. Therefore， this paper proposes a visual SLAM algorithm based on adaptive fusion of point and edge features. In the front end， a method based on grid method is proposed to evaluate the quality of point features， which is used to judge the texture of the external environment. In the back end， different visual constraints are constructed according to the external environment to optimize the camera pose. In addition， a distance transformation algorithm is introduced to construct the distance error function of edge features， which improves the speed of iterative optimization. This paper evaluates the visual SLAM algorithm on the most popular public datasets， and compares with the state-of-the-art methods. The experimental results show that the average positioning accuracy of the proposed algorithm is 22.3% higher than that of the state-of-the-art ORBSLAM algorithm in the weak texture environment， and it also achieves better positioning accuracy in the rich texture environment.

Key words: simultaneous localization and mapping, machine vision, mobile robot, edge features, weak texture