Grid-based Visual SLAM in Complex Environment

This paper presents a novel real-time SLAM method working in an unstructured indoor environment with a single forward viewing camera. Most existing visual SLAM methods extract features from the environment, associate them in different images and produce a feature map as a result. However, our approach estimates the distances between the robot and the obstacles by applying a visual sonar technique to the image and then associates this range data through an iterative closest point (ICP) based algorithm and finally produces the grid map. Here, we reconstruct a pseudo-dense scan pattern (PDSP) which is a temporal accumulation of the visual sonar readings based on odometry readings to overcome the sparseness of the visual sonar and then apply ICP based algorithm to associate this scan pattern with the previous one. In addition, we further correct the slight trajectory error included in the PDSP reconstruction step to get a much more refined map. Experimental results show that our method can obtain a grid map using a single camera alone unlike active sensor-based SLAMs