We describe and validate a simple context-based scene recognition algorithm for mobile robotics applications. The system can differentiate outdoor scenes from various sites on a college campus using a multiscale set of early-visual features, which capture the "gist" of the scene into a low-dimensional signature vector. Distinct from previous approaches, the algorithm presents the advantage of being biologically plausible and of having low-computational complexity, sharing its low-level features with a model for visual attention that may operate concurrently on a robot. We compare classification accuracy using scenes filmed at three outdoor sites on campus (13,965 to 34,711 frames per site). Dividing each site into nine segments, we obtain segment classification rates between 84.21 percent and 88.62 percent. Combining scenes from all sites (75,073 frames in total) yields 86.45 percent correct classification, demonstrating the generalization and scalability of the approach.

Abstract-We present a robot localization system using biologically-inspired vision. Our system models two extensively studied human visual capabilities: (1) extracting the "gist" of a scene to produce a coarse localization hypothesis, and (2) refining it by locating salient landmark points in the scene. Gist is computed here as a holistic statistical signature of the image, yielding abstract scene classification and layout. Saliency is computed as a measure of interest at every image location, efficiently directing the time-consuming landmark identification process towards the most likely candidate locations in the image. The gist features and salient regions are then further processed using a Monte-Carlo localization algorithm to allow the robot to generate its position. We test the system in three different outdoor environments -building complex (38.4x54.86m area, 13966 testing images), vegetation-filled park (82.3x109.73m area, 26397 testing images), and open-field park (137.16x178.31m area, 34711 testing images) -each with its own challenges. The system is able to localize, on average, within 0.98, 2.63, and 3.46m, respectively, even with multiple kidnapped-robot instances.

Abstract-We present a robot localization system using biologically-inspired vision. Our system models two extensively studied human visual capabilities: (1) extracting the "gist" of a scene to produce a coarse localization hypothesis, and (2) refining it by locating salient landmark regions in the scene. Gist is computed here as a holistic statistical signature of the image, yielding abstract scene classification and layout. Saliency is computed as a measure of interest at every image location, efficiently directing the time-consuming landmark identification process towards the most likely candidate locations in the image. The gist and salient landmark features are then further processed using a Monte-Carlo localization algorithm to allow the robot to generate its position. We test the system in three different outdoor environments -building complex (126x180ft. area, 3794 testing images), vegetation-filled park (270x360ft. area, 7196 testing images), and open-field park (450x585ft. area, 8287 testing images) -each with its own challenges. The system is able to localize, on average, within 6.0, 10.73, and 32.24 ft., respectively, even with multiple kidnapped-robot instances.

Abstract-We present a mobile robot navigation system guided by a novel vision-based road recognition approach. The system represents the road as a set of lines extrapolated from the detected image contour segments. These lines enable the robot to maintain its heading by centering the vanishing point in its field of view, and to correct the long term drift from its original lateral position. We integrate odometry and our visual road recognition system into a grid-based local map that estimates the robot pose as well as its surroundings to generate a movement path. Our road recognition system is able to estimate the road center on a standard dataset with 25,076 images to within 11.42 cm (with respect to roads at least 3 m wide). It outperforms three other state-of-the-art systems. In addition, we extensively test our navigation system in four busy college campus environments using a wheeled robot. Our tests cover more than 5 km of autonomous driving without failure. This demonstrates robustness of the proposed approach against challenges that include occlusion by pedestrians, non-standard complex road markings and shapes, shadows, and miscellaneous obstacle objects.

We present a monocular vision-based navigation system that incorporates two contrasting approaches: region segmentation that computes the road appearance, and road boundary detection that estimates the road shape. The former approach segments the image into multiple regions, then selects and tracks the most likely road appearance. On the other hand, the latter detects the vanishing point and road boundaries to estimate the shape of the road. Our algorithm operates in urban road settings and requires no training or camera calibration to maximize its adaptability to many environments. We tested our system in 1 indoor and 3 outdoor urban environments using our ground-based robot, Beobot 2.0, for real-time autonomous visual navigation. In 20 trial runs the robot was able to travel autonomously for 98.19% of the total route length of 316.60m.