Vision-based SLAM in Changing Outdoor Environments

For robots operating in outdoor environments, a number of factors such as weather, time of day, rough terrain, high speeds and hardware limitations make performing vision-based SLAM with current techniques infeasible due to factors such as image blur and/or underexposure, especially on smaller platforms and low cost hardware. In this paper we present novel visual place recognition and odometry techniques that address the challenges posed by low lighting, perceptual change and low cost cameras. Our primary contribution is a novel two-step algorithm that combines fast low resolution whole image matching with a higher resolution patch verification step, as well as image salience methods that simultaneously improve performance and decrease compute time. The algorithms are demonstrated using consumer cameras mounted on a small vehicle in a mixed urban and vegetated environment and a car traversing highway and suburban streets, at different times of day and night and in various weather conditions. The algorithms achieve reliable mapping over the course of a day, both when incrementally incorporating new visual scenes from different times of day into an existing map and when using a static map comprising visual scenes captured at only one point in time. Using the two-step place recognition process, we demonstrate for the first time single image, error-free place recognition at recall rates above 50% across a day-night dataset without prior training or utilization of image sequences. This place recognition performance enables topologically correct mapping across day-night cycles.