Precision Tracking with Sparse 3D and Dense Color 2D Data

Abstract

Precision tracking is important for predicting the behavior of other cars in autonomous driving. We present a novel method to combine laser and camera data to achieve accurate velocity estimates of moving vehicles. We combine sparse laser points with a high-resolution camera image to obtain a dense colored point cloud. We use a color-augmented search algorithm to align the dense color point clouds from successive time frames for a moving vehicle, thereby obtaining a precise estimate of the tracked vehicle’s velocity. Using this alignment method, we obtain velocity estimates at a much higher accuracy than previous methods. Through pre-filtering, we are able to achieve near real time results. We also present an online method for real-time use with accuracies close to that of the full method. We present a novel approach to quantitatively evaluate our velocity estimates by tracking a parked car in a local reference frame in which it appears to be moving relative to the ego vehicle. We use this evaluation method to automatically quantitatively evaluate our tracking performance on 466 separate tracked vehicles. Our method obtains a mean absolute velocity error of 0.27 m/s and an RMS error of 0.47 m/s on this test set. We can also qualitatively evaluate our method by building color 3D car models from moving vehicles. We have thus demonstrated that our method can be used for precision car tracking with applications to autonomous driving and behavior modeling.