Aquatic robots require an accurate and reliable localization system to navigate autonomously and perform practical missions. Kalman filters (KFs) and their variants are typically used in aquatic robots to combine sensor data. The two critical drawbacks of KFs are the requirement for skilled tuning of several filter parameters and the fact that changes to how the Inertial Measurement Unit (IMU) is oriented necessitate modifying the filter. To overcome those problems, this paper presents a novel method of fusing sensor data from a Doppler Velocity Log (DVL) and IMU using an adaptive nonlinear estimator to provide dead reckoning localization for a small autonomous surface vehicle. The proposed method has only one insensitive tuning parameter and is agnostic to the configuration of the IMU. The system was validated using a small ASV in a 2.4×3.6×2.4 m water tank, with a motion capture system as ground truth, and was evaluated against a state-of-the-art method based on KFs. Experiments showed that the average drift error of the nonlinear filter was 0.16 m (s.d. 0.06 m) compared to 0.15 m (s.d. 0.05 m) for the state of the art, meaning that the benefits in terms of tuning and flexible configuration do not come at the expense of performance.