Light Detection and Ranging (LiDAR) sensors have become the sensor of choice for many robotic state estimation tasks. Because of this, in recent years there has been significant work done to find the most accurate method to perform state estimation using these sensors. In each of these prior works, an explosion of possible technique combinations has occurred, with each work comparing LiDAR Odometry (LO) "pipelines" to prior "pipelines". Unfortunately, little work up to this point has performed the significant amount of ablation studies comparing the various building-blocks of a LO pipeline. In this work, we summarize the various techniques that go into defining a LO pipeline and empirically evaluate these LO components on an expansive number of datasets across environments, LiDAR types, and vehicle motions. Finally, we make empirically-backed recommendations for the design of future LO pipelines to provide the most accurate and reliable performance.

Due to the difficulty and expense of underwater field trials, a high fidelity underwater simulator is a necessity for testing and developing algorithms. To fill this need, we present HoloOcean, an open source underwater simulator, built upon Unreal Engine 4 (UE4). HoloOcean comes equipped with multi-agent support, various sensor implementations of common underwater sensors, and simulated communications support. We also implement a novel sonar sensor model that leverages an octree representation of the environment for efficient and realistic sonar imagery generation. Due to being built upon UE4, new environments are straightforward to add, enabling easy extensions to be built. Finally, HoloOcean is controlled via a simple python interface, allowing simple installation via pip, and requiring few lines of code to execute simulations.

Sonar sensors play an integral part in underwater robotic perception by providing imagery at long distances where standard optical cameras cannot. They have proven to be an important part in various robotic algorithms including localization, mapping, and structure from motion. Unfortunately, generating realistic sonar imagery for algorithm development is difficult due to the high cost of field trials and lack of simulation methods. To remove these obstacles, we present various upgrades to the sonar simulation method in HoloOcean, our open-source marine robotics simulator. In particular, we improve the noise modeling using a novel cluster-based multipath ray-tracing algorithm, various probabilistic noise models, and material dependence. We also develop and integrate simulated models for side-scan, single-beam, and multibeam profiling sonars.