The widespread deployment of wireless networks presents an opportunity for localization and mapping using only signal-strength measurements. The current state of the art is to use Gaussian process latent variable models (GP-LVM). This method works well, but relies on a signature uniqueness assumption which limits its applicability to only signal-rich environments. Moreover, it does not scale computationally to large sets of data, requiring O (N3) operations per iteration. We present a GraphSLAM-like algorithm for signal strength SLAM. Our algorithm shares many of the benefits of Gaussian processes, yet is viable for a broader range of environments since it makes no signature uniqueness assumptions. It is also more tractable to larger map sizes, requiring O (N2) operations per iteration. We compare our algorithm to a laser-SLAM ground truth, showing it produces excellent results in practice.

In order to achieve autonomous operation of a vehicle in urban situations with unpredictable traffic, several realtime systems must interoperate, including environment perception, localization, planning, and control. In addition, a robust vehicle platform with appropriate sensors, computational hardware, networking, and software infrastructure is essential. We previously published an overview of Junior, Stanford's entry in the 2007 DARPA Urban Challenge. This race was a closed-course competition which, while historic and inciting much progress in the field, was not fully representative of the situations that exist in the real world. In this paper, we present a summary of our recent research towards the goal of enabling safe and robust autonomous operation in more realistic situations. First, a trio of unsupervised algorithms automatically calibrates our 64-beam rotating LIDAR with accuracy superior to tedious hand measurements. We then generate high-resolution maps of the environment which are subsequently used for online localization with centimeter accuracy. Improved perception and recognition algorithms now enable Junior to track and classify obstacles as cyclists, pedestrians, and vehicles; traffic lights are detected as well. A new planning system uses this incoming data to generate thousands of candidate trajectories per second, choosing the optimal path dynamically. The improved controller continuously selects throttle, brake, and steering actuations that maximize comfort and minimize trajectory error. All of these algorithms work in sun or rain and during the day or night. With these systems operating together, Junior has successfully logged hundreds of miles of autonomous operation in a variety of real-life conditions.

We present an algorithm that learns invariant features from real data in an entirely unsupervised fashion. The principal benefit of our method is that it can be applied without human intervention to a particular application or data set, learning the specific invariances necessary for excellent feature performance on that data. Our algorithm relies on the ability to track image patches over time using optical flow. With the wide availability of high frame rate video (eg: on the web, from a robot), good tracking is straightforward to achieve. The algorithm then optimizes feature parameters such that patches corresponding to the same physical location have feature descriptors that are as similar as possible while simultaneously maximizing the distinctness of descriptors for different locations. Thus, our method captures data or application specific invariances yet does not require any manual supervision. We apply our algorithm to learn domain-optimized versions of SIFT and HOG. SIFT and HOG features are excellent and widely used. However, they are general and by definition not tailored to a specific domain. Our domain-optimized versions offer a substantial performance increase for classification and correspondence tasks we consider. Furthermore, we show that the features our method learns are near the optimal that would be achieved by directly optimizing the test set performance of a classifier. Finally, we demonstrate that the learning often allows fewer features to be used for some tasks, which has the potential to dramatically improve computational concerns for very large data sets.

The interpretation of uncertain sensor streams for localization is usually considered in the context of a robot. Increasingly, however, portable consumer electronic devices, such as smartphones, are equipped with sensors including WiFi radios, cameras, and inertial measurement units (IMUs). Many tasks typically associated with robots, such as localization, would be valuable to perform on such devices. In this paper, we present an approach for indoor localization exclusively using the low-cost sensors typically found on smartphones. Environment modification is not needed. We rigorously evaluate our method using ground truth acquired using a laser range scanner. Our evaluation includes overall accuracy and a comparison of the contribution of individual sensors. We find experimentally that fusion of multiple sensor modalities is necessary for optimal performance and demonstrate sub-meter localization accuracy.

We present a method for learning activity-based ground models based on a multiple particle filter approach to motion tracking in video acquired from a moving aerial platform. Such models offer a number of potential benefits. In this paper we demonstrate the ability of activity-based models to improve the performance of an object motion tracker as well as their applicability to global registration of video sequences.