Mapless Localization from Fused Sensor Data

Mapless Localization from Fused Sensor Data Arvind Antonio de Menezes Pereira, Onur Sert and Ilya Eckstein

CS547: Sensing and Planning in Robotics Project Report Instructor: Gaurav Sukhatme

Introduction With the advent of inexpensive digital cameras and powerful processors, vision-based techniques for localization and navigation are becoming increasingly attractive. In this project, we set out to explore the vision-based approach in the context of autonomous localization. We base our analysis on a reportedly successful visual SLAM method by Se et al.[1], and ask the following question: can we use visual landmarks while doing localization only, without constructing a map? In addition to digital cameras, our system allows integration of data from multiple sensors, providing the ability to compare visionbased vs. blind localization, as well as experiment with different combinations of sensors and their effect on localization accuracy.

Experimental setup Our experiments were out are carried using Player interface and Gazebo simulator, using an artificially constructed world. The world is a simple rectangle corridor of outer dimensions 30 m by 20 m and a thickness of 5 meters, approximately. The walls are covered with several images mimicking indoor and outdoor environments, to be used by the stereo camera for state estimation (see Figure 1).

Figure 1

Left, an aerial view f the world. Right, frontal camera view.

Sensors: The sensors used are odometer (of a Pioneer2 robot), GPS and a stereo camera, which make use of a SIFT algorithm implementation in order to calculate the position of the robot.

Approach Following our starting point [1], our approach is uses Kalman filtering. Below we describe our action and sensing models. The project can be divided in two major parts: visual localization landmarks [1] (which Vistrack) as opposed blind navigation (BlindTrack). Action Model: Odometry based, while the parameters are assumed to follow Gaussian distribution [1]. Sensing Model: Stereo Cameras Using a binocular view stereo camera, at each time step we obtain a pair of corresponding images. From each image, a set of SIFT features [2] is extracted (see Figure 2). The resulted two feature sets are stereo-matched, yielding a more compact set of 3D landmarks. In next time step, odometry-based prediction of the camera pose used to reproject the landmarks onto the new view. Matching of the reprojected landmarks against the new landmark set results in a number of correspondences in the image plane. Finally, a numerical least square optimization is invoked to minimize the distance between the corresponding 2D positions, as a function of the change in camera pose. The estimated pose displacement is applied as a correction to the odometry prediction. Note that, as we are not building a map, landmarks are only use for estimation of displacement between subsequent frames.

Figure 2 SIFT features extracted from subsequent camera views. GPS We use GPS as our “blind” source. This both provides a high standard for comparison and illustrates a real life setting (e.g., indoor / outdoor localization). We model the GPS error using Gaussinan distribution, while the are based on heuristics such as HDOP (Horizontal dilution of Precision) and velocity-based confidence for the heading.

Trials and Discussion: We will now discuss some of the phenomena in localization we have observed in our trials. The trials are categorized depending on the sensor setup used in the trial. Odometry only: Figure 3 shows the estimated position and uncertainty ellipses of the robot while it moves around the corridor in a closed loop. The small black dots show the actual path that the robot traverses.

Figure 3 Odometry error We can see that the robot pose estimation is unsatisfactory, if we use only the odometer as our sensor. The state rapidly deviates from the actual pose and we see an accumulation of error in the robot pose. The uncertainty ellipses correspond to X/Y axes. We can see that pose uncertainty is the largest along the direction of movement, finally growing beyond our display size. Odometry plus Stereo Cameras: The conclusion we have reached through our experiments with inter-frame visual landmarks is simple: in our setup, without maintaining a landmark database (map), the method is not satisfactory for robust localization over time. Figure 4 shows an accumulating error in pose estimation over time. The reasons for this phenomenon are two-fold: a) What the method estimates is not the pose, but the pose displacement with respect to the previous state (image). Therefore, it accumulates drift by definition. b) The method is extremely sensitive to the resilience of landmarks over time. By giving up the map database, we limit landmark life span to two frames only,

thus not being able to track landmark uncertainty over time. c) Conclusion: Maintaining an (incremental) map is necessary to ensure a robust performance. Nevertheless, the method is still promising when being allowed occasional recoveries using other sensors, e.g. GPS. We present analysis of the GPS-based localization in the next section.

Figure 4 Error in vision-based estimate as a function of time

GPS plus Odometry: Below is a screenshot obtained by using both the odometer and GPS. The GPS estimate and odometry-based estimates are shown in red and green, respectively. We take one odometer and one GPS reading, one after another. Again we start to move from (0,0,) and as we see from the image, this time we succeed in localizing the robot on the map, as expected.

Figure 5 Odometry + GPS

On the left, we see that error does not accumulate when using a GPS sensor. On the right, we can see that the odometery estimation error increases in time. The below figure shows a case where the initial pose of the robot was wrongly estimated (with perfect confidence, shown as the robot in black at (0,0)). By using GPS, we make sure that the robot recovers from this wrong estimation. This means that we can recover from any loss event by using the GPS. Convergence may take more than one GPS measurement.

Bibliography: [1] Mobile robot localization and mapping with uncertainty using scaleinvariant visual landmarks. Stephen Se, David G. Lowe and Jim Little, International Journal of Robotics Research, 21, 8 (2002), pp. 735-758. [2] Object recognition from local scale-invariant features, David G. Lowe, International Conference on Computer Vision, Corfu, Greece (September 1999), pp. 1150-1157.