Online Informative Sampling using Semantic Features in Underwater Environments

The underwater world remains largely unexplored, with Autonomous Underwater Vehicles (AUVs) playing a crucial role in sub-sea explorations. However, continuous monitoring of underwater environments using AUVs can generate a significant amount of data. In addition, sending live data feed from an underwater environment requires dedicated on-board data storage options for AUVs which can hinder requirements of other higher priority tasks. Informative sampling techniques offer a solution by condensing observations. In this paper, we present a semantically-aware online informative sampling (ON-IS) approach which samples an AUV's visual experience in real-time. Specifically, we obtain visual features from a fine-tuned object detection model to align the sampling outcomes with the desired semantic information. Our contributions are (a) a novel Semantic Online Informative Sampling (SON-IS) algorithm, (b) a user study to validate the proposed approach and (c) a novel evaluation metric to score our proposed algorithm with respect to the suggested samples by human subjects

Evaluating Visual Odometry Methods for Autonomous Driving in Rain

The increasing demand for autonomous vehicles has created a need for robust navigation systems that can also operate effectively in adverse weather conditions. Visual odometry is a technique used in these navigation systems, enabling the estimation of vehicle position and motion using input from onboard cameras. However, visual odometry accuracy can be significantly impacted in challenging weather conditions, such as heavy rain, snow, or fog. In this paper, we evaluate a range of visual odometry methods, including our DROIDSLAM based heuristic approach. Specifically, these algorithms are tested on both clear and rainy weather urban driving data to evaluate their robustness. We compiled a dataset comprising of a range of rainy weather conditions from different cities. This includes, the Oxford Robotcar dataset from Oxford, the 4Seasons dataset from Munich and an internal dataset collected in Singapore. We evaluated different visual odometry algorithms for both monocular and stereo camera setups using the Absolute Trajectory Error (ATE). Our evaluation suggests that the Depth and Flow for Visual Odometry (DF-VO) algorithm with monocular setup worked well for short range distances (< 500m) and our proposed DROID-SLAM based heuristic approach for the stereo setup performed relatively well for long-term localization. Both algorithms performed consistently well across all rain conditions.

Localization with Anticipation for Autonomous Urban Driving in Rain

This paper presents a localization algorithm for autonomous urban vehicles under rain weather conditions. In adverse weather, human drivers anticipate the location of the ego-vehicle based on the control inputs they provide and surrounding road contextual information. Similarly, in our approach for localization in rain weather, we use visual data, along with a global reference path and vehicle motion model for anticipating and better estimating the pose of the ego-vehicle in each frame. The global reference path contains useful road contextual information such as the angle of turn which can be potentially used to improve the localization accuracy especially when sensors are compromised. We experimented on the Oxford Robotcar Dataset and our internal dataset from Singapore to validate our localization algorithm in both clear and rain weather conditions. Our method improves localization accuracy by 50.83% in rain weather and 34.32% in clear weather when compared to baseline algorithms.