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Integrated Stereovision for an Autonomous Ground Vehicle 2005 SURF Report

This report describes development of the stereovision sensing system on an autonomous vehicle competing in the DARPA Grand Challenge, a desert race for robotic vehicles. A basic stereovision system was in place when the project started, but it had not been tested on the new vehicle. It turned out a lot of problems needed to be solved before stereovision was ready to be integrated into the full system. New algorithms were designed and implemented to improve camera focus and stereovision processing, and to remove noise in the data. A lot of testing was performed to ensure data quality before integration into the sensor suite of the vehicle. Results show that, even though the vehicle is capable of obstacle avoidance and navigation using only stereovision sensing, the quality of stereovision data needs to be improved Introduction To stimulate research and innovation in the field of autonomous ground vehicles, the DARPA (Defense Advanced Research Projects Agency) announced in March 2003 that it would arrange a competition known as the DARPA Grand Challenge (DGC). The competition would be an offroad race for autonomous vehicles held in the desert. A first race was held a year later, which none of the participants were able to complete. A second race was held on the 8th of October 2005. A team consisting of mainly Caltech undergraduates, named Team Caltech, competed in both races. The work described in this report was performed during the summer of 2005 and the remaining time after the summer before the race. Team Caltech’s vehicle for the 2005 DGC, called Alice, is a 2005 Ford E-350 van that has been modified by Team Caltech sponsor Sportsmobile, Inc. for offroad usage. Modifications include custom suspension and transmission, plus electrical power and air handling systems. In order for Alice to drive autonomously, she has been equipped with actuators, a computer cluster and a range of sensors, including four cameras used for stereovision processing. Problem Description and Approach Alice uses internal maps of the surrounding terrain to plan her driving. The objective of stereovision is to help create these maps using images from cameras in stereo pairs. Given knowledge on how the three-dimensional world is transformed into the images, the images are used to reconstruct geometrical information about the world. Finding the parameters that describe the transformation is known as camera calibration. Alice’s stereovision system uses a software called Small Vision System (SVS) from Videre Design Inc. that performs major parts of the calibration and the stereo processing. Figure 1: Simple flowchart of stereovision processing in Alice In the beginning of the summer of 2005, the stereovision system that was used in 2004 had not yet been tested on Alice. In order for stereovision to be used in the 2005 race, there was need for new hardware, better calibration, good parameter settings, system integration, and lots of testing. The approach carried out was to try to break down all the problems into separate issues and to always work on the issue that seemed to be the biggest obstacle towards a well-functioning integrated system. Due to the pressing deadline (the race), simple solutions were often chosen rather than more elegant, but time-consuming, ones. In general, solutions were first tested in simulation or in simple terrain (i.e. a parking lot) before attempting the desert environment. System Design and Analysis Camera Essentials The old camera lenses were not sturdy and thus inappropriate for rough driving. It was therefore decided to purchase new lenses. The focal length of the new lenses was selected to be 2.8 mm and 8 mm for the short and long range camera pairs respectively. In combination with baselines (the distance between a camera pair) of 0.6 m and 1.5 m, this approximately gives range resolutions of 0.2 m at 12 m and 37 m range for short and long range respectively. Figure 2: The plot shows a measure of the contrast in the red box as the focus was changed. To achieve optimal focus at the desired ranges visual inspection of the images was not considered to be sufficient. A semi-automatic focus algorithm was implemented in MATLAB. The algorithm continuously computes a simple contrast measure (the sum of the absolute values of the 100 largest intensity differences) on a selected part of the image, and plots the measure in real-time as the focus knob on the lens is manually turned. With feedback from the plots, it was easy to find optimal focus settings. Stereo Image pairs 3D Point Clouds Terrain map Camera Calibration