figure 6. Course map provided by DARPA, here shown with our data processing tool.

figure 8. Lateral localization in Junior, based on infrared remission values acquired by the laser. the yellow graph depicts the posterior lateral position estimate.

visible in the laser scans to map features, to further refine localization.

Stanley’s localization only addresses the lateral location of the robot relative to the map. Figure 7 illustrates the analysis of the terrain for a discrete set of vertical offsets. Localization then adjusts the estimated INS pose estimates such that the center line of the road in the map aligns with the center of the drivable corridor. As a result, Stanley tends to stay centered on the road (unless, of course, the robot swerves to avoid an obstacle).

Junior’s localization is essentially identical, but using infrared remission values of the laser in addition to range-based obstacle features. Infrared remission facilitates

figure 7. Localization uses momentary sensor data to estimate the location of the robot relative to the map with centimeter precision. in Stanley, the localization only estimated the lateral location, as indicated by the lateral offset bars.

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the detection of lane markings, which are not detectable with range. Figure 8 illustrates an infrared remission scan, superimposed with the localization results. The yellow curve in this figure represents the posterior distribution over the lateral offset to the map, as estimated by fusing INS and remission values. In this specific instance, the localizer reduces the GPS error from about a meter to a few centimeters.

3. 5. obstacle tracking

Roads are full of obstacles. Many are static, such as ruts and berms in the desert, or curbs and parked vehicles in an urban environment. To avoid such static obstacles, both vehicles build local occupancy grid maps8 that maintain the location of static obstacles. Figure 9 shows examples of a maps built by both robots. Whereas Stanley distinguished only three types of terrain—drivable, occupied, and unexplored— Junior also categorizes the type of obstacles by height, which leads to an approximate distinction of curbs, cars, and tall trees, as illustrated in Figure 9b.

Equally relevant is the tracking of moving objects such as cars, which play a major role in urban driving. The key element of detecting moving objects is temporal differencing. If two subsequent laser scans mark a region as free in one scan, and occupied in another, then this joint observation constitutes a potential “witness” of a moving object. For the situation depicted in Figure 10a, Figure 10b illustrates such an analysis. Here scan points colored red or green correspond to such witnesses. The set of witnesses is then filtered (e.g., points outside the drivable map are removed) and moving objects are then tracked using particle filters. Figure 10c depicts an example result in which Junior finds and tracks four vehicles.

Further vehicle tracking is provided using radar sensors. To this end, Junior possesses three radar detectors, one pointing straight ahead, and two pointing to each side. The radars provide redundancy in moving object detection, and hence enhance the vehicle’s reliability when merging into moving traffic; however, they are only used