Learning a Bias Correction for Lidar-only Motion Estimation

This paper presents a novel technique to correct for bias in a classical estimator using a learning approach. We apply a learned bias correction to a lidar-only motion estimation pipeline. Our technique trains a Gaussian process (GP) regression model using data with ground truth. The inputs to the model are high-level features derived from the geometry of the point-clouds, and the outputs are the predicted biases between poses computed by the estimator and the ground truth. The predicted biases are applied as a correction to the poses computed by the estimator. Our technique is evaluated on over 50km of lidar data, which includes the KITTI odometry benchmark and lidar datasets collected around the University of Toronto campus. After applying the learned bias correction, we obtained significant improvements to lidar odometry in all datasets tested. We achieved around 10% reduction in errors on all datasets from an already accurate lidar odometry algorithm, at the expense of only less than 1% increase in computational cost at run-time.Comment: 15th Conference on Computer and Robot Vision (CRV 2018

Benchmarking ground truth trajectories with robotic total stations

Benchmarks stand as vital cornerstones in elevating SLAM algorithms within mobile robotics. Consequently, ensuring accurate and reproducible ground truth generation is vital for fair evaluation. A majority of outdoor ground truths are generated by GNSS, which can lead to discrepancies over time, especially in covered areas. However, research showed that RTS setups are more precise and can alternatively be used to generate these ground truths. In our work, we compare both RTS and GNSS systems' precision and repeatability through a set of experiments conducted weeks and months apart in the same area. We demonstrated that RTS setups give more reproducible results, with disparities having a median value of 8.6 mm compared to a median value of 10.6 cm coming from a GNSS setup. These results highlight that RTS can be considered to benchmark process for SLAM algorithms with higher precision